Overview

Dataset statistics

Number of variables64
Number of observations1000
Missing cells27247
Missing cells (%)42.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory500.1 KiB
Average record size in memory512.1 B

Variable types

Numeric10
Categorical54

Alerts

exposure_number has constant value ""Constant
fire_fatalities has constant value ""Constant
fire_injuries has constant value ""Constant
civilian_fatalities has constant value ""Constant
civilian_injuries has constant value ""Constant
number_of_alarms has constant value ""Constant
mutual_aid has constant value ""Constant
ignition_factor_secondary has constant value ""Constant
number_of_floors_with_heavy_damage has constant value ""Constant
number_of_floors_with_extreme_damage has constant value ""Constant
detector_failure_reason has constant value ""Constant
automatic_extinguishing_sytem_type has constant value ""Constant
automatic_extinguishing_sytem_perfomance has constant value ""Constant
automatic_extinguishing_sytem_failure_reason has constant value ""Constant
number_of_sprinkler_heads_operating has constant value ""Constant
address has a high cardinality: 865 distinct valuesHigh cardinality
alarm_dttm has a high cardinality: 1000 distinct valuesHigh cardinality
arrival_dttm has a high cardinality: 997 distinct valuesHigh cardinality
close_dttm has a high cardinality: 998 distinct valuesHigh cardinality
first_unit_on_scene has a high cardinality: 81 distinct valuesHigh cardinality
primary_situation has a high cardinality: 85 distinct valuesHigh cardinality
property_use has a high cardinality: 62 distinct valuesHigh cardinality
point has a high cardinality: 856 distinct valuesHigh cardinality
incident_number is highly overall correlated with id and 4 other fieldsHigh correlation
id is highly overall correlated with incident_number and 4 other fieldsHigh correlation
call_number is highly overall correlated with incident_number and 4 other fieldsHigh correlation
zipcode is highly overall correlated with floor_of_fire_origin and 3 other fieldsHigh correlation
suppression_units is highly overall correlated with suppression_personnel and 3 other fieldsHigh correlation
suppression_personnel is highly overall correlated with suppression_units and 1 other fieldsHigh correlation
ems_personnel is highly overall correlated with ems_unitsHigh correlation
supervisor_district is highly overall correlated with city and 10 other fieldsHigh correlation
floor_of_fire_origin is highly overall correlated with zipcode and 7 other fieldsHigh correlation
box is highly overall correlated with city and 22 other fieldsHigh correlation
incident_date is highly overall correlated with incident_number and 10 other fieldsHigh correlation
city is highly overall correlated with supervisor_district and 23 other fieldsHigh correlation
battalion is highly overall correlated with supervisor_district and 6 other fieldsHigh correlation
station_area is highly overall correlated with zipcode and 5 other fieldsHigh correlation
ems_units is highly overall correlated with ems_personnelHigh correlation
other_units is highly overall correlated with other_personnel and 3 other fieldsHigh correlation
other_personnel is highly overall correlated with other_units and 2 other fieldsHigh correlation
first_unit_on_scene is highly overall correlated with zipcode and 6 other fieldsHigh correlation
primary_situation is highly overall correlated with suppression_personnel and 4 other fieldsHigh correlation
action_taken_primary is highly overall correlated with detector_operationHigh correlation
action_taken_secondary is highly overall correlated with box and 4 other fieldsHigh correlation
action_taken_other is highly overall correlated with floor_of_fire_origin and 5 other fieldsHigh correlation
detector_alerted_occupants is highly overall correlated with box and 1 other fieldsHigh correlation
property_use is highly overall correlated with structure_type and 2 other fieldsHigh correlation
neighborhood_district is highly overall correlated with zipcode and 6 other fieldsHigh correlation
estimated_contents_loss is highly overall correlated with incident_number and 11 other fieldsHigh correlation
area_of_fire_origin is highly overall correlated with suppression_units and 10 other fieldsHigh correlation
ignition_cause is highly overall correlated with box and 8 other fieldsHigh correlation
ignition_factor_primary is highly overall correlated with box and 7 other fieldsHigh correlation
heat_source is highly overall correlated with floor_of_fire_origin and 11 other fieldsHigh correlation
item_first_ignited is highly overall correlated with floor_of_fire_origin and 15 other fieldsHigh correlation
human_factors_associated_with_ignition is highly overall correlated with box and 8 other fieldsHigh correlation
estimated_property_loss is highly overall correlated with incident_number and 10 other fieldsHigh correlation
structure_type is highly overall correlated with suppression_units and 19 other fieldsHigh correlation
structure_status is highly overall correlated with box and 14 other fieldsHigh correlation
fire_spread is highly overall correlated with suppression_units and 15 other fieldsHigh correlation
no_flame_spead is highly overall correlated with supervisor_district and 7 other fieldsHigh correlation
number_of_floors_with_minimum_damage is highly overall correlated with supervisor_district and 6 other fieldsHigh correlation
number_of_floors_with_significant_damage is highly overall correlated with supervisor_district and 6 other fieldsHigh correlation
detectors_present is highly overall correlated with box and 13 other fieldsHigh correlation
detector_type is highly overall correlated with box and 6 other fieldsHigh correlation
detector_operation is highly overall correlated with box and 12 other fieldsHigh correlation
detector_effectiveness is highly overall correlated with box and 9 other fieldsHigh correlation
automatic_extinguishing_system_present is highly overall correlated with box and 10 other fieldsHigh correlation
city is highly imbalanced (96.5%)Imbalance
ems_units is highly imbalanced (84.7%)Imbalance
other_units is highly imbalanced (92.1%)Imbalance
other_personnel is highly imbalanced (91.4%)Imbalance
action_taken_primary is highly imbalanced (56.6%)Imbalance
action_taken_secondary is highly imbalanced (93.3%)Imbalance
action_taken_other is highly imbalanced (98.3%)Imbalance
detector_alerted_occupants is highly imbalanced (78.8%)Imbalance
estimated_contents_loss is highly imbalanced (98.2%)Imbalance
estimated_property_loss is highly imbalanced (98.5%)Imbalance
first_unit_on_scene has 121 (12.1%) missing valuesMissing
supervisor_district has 781 (78.1%) missing valuesMissing
estimated_contents_loss has 61 (6.1%) missing valuesMissing
area_of_fire_origin has 950 (95.0%) missing valuesMissing
ignition_cause has 950 (95.0%) missing valuesMissing
ignition_factor_primary has 950 (95.0%) missing valuesMissing
ignition_factor_secondary has 950 (95.0%) missing valuesMissing
heat_source has 950 (95.0%) missing valuesMissing
item_first_ignited has 950 (95.0%) missing valuesMissing
human_factors_associated_with_ignition has 950 (95.0%) missing valuesMissing
estimated_property_loss has 62 (6.2%) missing valuesMissing
structure_type has 974 (97.4%) missing valuesMissing
structure_status has 974 (97.4%) missing valuesMissing
floor_of_fire_origin has 985 (98.5%) missing valuesMissing
fire_spread has 974 (97.4%) missing valuesMissing
no_flame_spead has 985 (98.5%) missing valuesMissing
number_of_floors_with_minimum_damage has 985 (98.5%) missing valuesMissing
number_of_floors_with_significant_damage has 985 (98.5%) missing valuesMissing
number_of_floors_with_heavy_damage has 985 (98.5%) missing valuesMissing
number_of_floors_with_extreme_damage has 985 (98.5%) missing valuesMissing
detectors_present has 974 (97.4%) missing valuesMissing
detector_type has 974 (97.4%) missing valuesMissing
detector_operation has 974 (97.4%) missing valuesMissing
detector_effectiveness has 974 (97.4%) missing valuesMissing
detector_failure_reason has 974 (97.4%) missing valuesMissing
automatic_extinguishing_system_present has 974 (97.4%) missing valuesMissing
automatic_extinguishing_sytem_type has 974 (97.4%) missing valuesMissing
automatic_extinguishing_sytem_perfomance has 974 (97.4%) missing valuesMissing
automatic_extinguishing_sytem_failure_reason has 974 (97.4%) missing valuesMissing
number_of_sprinkler_heads_operating has 985 (98.5%) missing valuesMissing
box has 989 (98.9%) missing valuesMissing
address is uniformly distributedUniform
alarm_dttm is uniformly distributedUniform
arrival_dttm is uniformly distributedUniform
close_dttm is uniformly distributedUniform
point is uniformly distributedUniform
incident_number has unique valuesUnique
id has unique valuesUnique
call_number has unique valuesUnique
alarm_dttm has unique valuesUnique
ems_personnel has 955 (95.5%) zerosZeros

Reproduction

Analysis started2023-05-09 20:11:11.452264
Analysis finished2023-05-09 20:11:53.616350
Duration42.16 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

incident_number
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3320889.9
Minimum3000003
Maximum8028926
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-05-09T22:11:53.804962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3000003
5-th percentile3000292
Q13002156
median3004653.5
Q33007226.8
95-th percentile8028175.1
Maximum8028926
Range5028923
Interquartile range (IQR)5070.75

Descriptive statistics

Standard deviation1221273.5
Coefficient of variation (CV)0.3677549
Kurtosis11.001049
Mean3320889.9
Median Absolute Deviation (MAD)2545
Skewness3.6026341
Sum3.3208899 × 109
Variance1.491509 × 1012
MonotonicityNot monotonic
2023-05-09T22:11:54.036973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8028304 1
 
0.1%
3002190 1
 
0.1%
3001465 1
 
0.1%
3001469 1
 
0.1%
3001470 1
 
0.1%
3001477 1
 
0.1%
3001479 1
 
0.1%
3001482 1
 
0.1%
3002177 1
 
0.1%
3002179 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
3000003 1
0.1%
3000006 1
0.1%
3000007 1
0.1%
3000014 1
0.1%
3000016 1
0.1%
3000020 1
0.1%
3000021 1
0.1%
3000025 1
0.1%
3000026 1
0.1%
3000040 1
0.1%
ValueCountFrequency (%)
8028926 1
0.1%
8028920 1
0.1%
8028918 1
0.1%
8028913 1
0.1%
8028912 1
0.1%
8028908 1
0.1%
8028900 1
0.1%
8028894 1
0.1%
8028882 1
0.1%
8028863 1
0.1%

exposure_number
Categorical

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
1000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1000
100.0%

Length

2023-05-09T22:11:54.226684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T22:11:54.381362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1000
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1000
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1000
100.0%

id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33208899
Minimum30000030
Maximum80289260
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-05-09T22:11:54.537927image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum30000030
5-th percentile30002920
Q130021560
median30046535
Q330072268
95-th percentile80281751
Maximum80289260
Range50289230
Interquartile range (IQR)50707.5

Descriptive statistics

Standard deviation12212735
Coefficient of variation (CV)0.3677549
Kurtosis11.001049
Mean33208899
Median Absolute Deviation (MAD)25450
Skewness3.6026341
Sum3.3208899 × 1010
Variance1.491509 × 1014
MonotonicityNot monotonic
2023-05-09T22:11:54.751480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80283040 1
 
0.1%
30021900 1
 
0.1%
30014650 1
 
0.1%
30014690 1
 
0.1%
30014700 1
 
0.1%
30014770 1
 
0.1%
30014790 1
 
0.1%
30014820 1
 
0.1%
30021770 1
 
0.1%
30021790 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
30000030 1
0.1%
30000060 1
0.1%
30000070 1
0.1%
30000140 1
0.1%
30000160 1
0.1%
30000200 1
0.1%
30000210 1
0.1%
30000250 1
0.1%
30000260 1
0.1%
30000400 1
0.1%
ValueCountFrequency (%)
80289260 1
0.1%
80289200 1
0.1%
80289180 1
0.1%
80289130 1
0.1%
80289120 1
0.1%
80289080 1
0.1%
80289000 1
0.1%
80288940 1
0.1%
80288820 1
0.1%
80288630 1
0.1%

address
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct865
Distinct (%)86.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
3rd St. / Howard St.
 
5
Market St. / Mason St.
 
5
Broad St. / Capitol Av.
 
5
18th St. / Mission St.
 
5
2300 16th St.
 
4
Other values (860)
976 

Length

Max length34
Median length28
Mean length18.197
Min length10

Characters and Unicode

Total characters18197
Distinct characters64
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique763 ?
Unique (%)76.3%

Sample

1st row150 Elsie St.
2nd row85 Turner Tr.
3rd row175 6th St.
4th row633 Hayes St.
5th row27th Av. / Cabrillo St.

Common Values

ValueCountFrequency (%)
3rd St. / Howard St. 5
 
0.5%
Market St. / Mason St. 5
 
0.5%
Broad St. / Capitol Av. 5
 
0.5%
18th St. / Mission St. 5
 
0.5%
2300 16th St. 4
 
0.4%
1 South Van Ness Av. 4
 
0.4%
Exeter St. / Paul Av. 3
 
0.3%
1 Duboce Av. 3
 
0.3%
375 Laguna Honda Bl. 3
 
0.3%
Ellis St. / Jones St. 3
 
0.3%
Other values (855) 960
96.0%

Length

2023-05-09T22:11:54.949872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
st 959
24.6%
394
 
10.1%
av 296
 
7.6%
bl 53
 
1.4%
mission 45
 
1.2%
market 35
 
0.9%
van 29
 
0.7%
ness 28
 
0.7%
3rd 27
 
0.7%
dr 24
 
0.6%
Other values (909) 2012
51.6%

Most occurring characters

ValueCountFrequency (%)
2902
 
15.9%
t 1585
 
8.7%
. 1356
 
7.5%
S 1077
 
5.9%
a 874
 
4.8%
e 693
 
3.8%
o 648
 
3.6%
r 636
 
3.5%
n 628
 
3.5%
l 482
 
2.6%
Other values (54) 7316
40.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8463
46.5%
Space Separator 2902
 
15.9%
Uppercase Letter 2632
 
14.5%
Decimal Number 2449
 
13.5%
Other Punctuation 1743
 
9.6%
Dash Punctuation 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 1585
18.7%
a 874
10.3%
e 693
8.2%
o 648
 
7.7%
r 636
 
7.5%
n 628
 
7.4%
l 482
 
5.7%
s 469
 
5.5%
i 431
 
5.1%
v 364
 
4.3%
Other values (16) 1653
19.5%
Uppercase Letter
ValueCountFrequency (%)
S 1077
40.9%
A 326
 
12.4%
B 145
 
5.5%
M 137
 
5.2%
C 112
 
4.3%
P 95
 
3.6%
G 93
 
3.5%
H 75
 
2.8%
F 63
 
2.4%
V 62
 
2.4%
Other values (14) 447
17.0%
Decimal Number
ValueCountFrequency (%)
1 464
18.9%
0 335
13.7%
2 322
13.1%
5 275
11.2%
3 266
10.9%
4 204
8.3%
6 169
 
6.9%
9 159
 
6.5%
7 129
 
5.3%
8 126
 
5.1%
Other Punctuation
ValueCountFrequency (%)
. 1356
77.8%
/ 387
 
22.2%
Space Separator
ValueCountFrequency (%)
2902
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11095
61.0%
Common 7102
39.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 1585
14.3%
S 1077
 
9.7%
a 874
 
7.9%
e 693
 
6.2%
o 648
 
5.8%
r 636
 
5.7%
n 628
 
5.7%
l 482
 
4.3%
s 469
 
4.2%
i 431
 
3.9%
Other values (40) 3572
32.2%
Common
ValueCountFrequency (%)
2902
40.9%
. 1356
19.1%
1 464
 
6.5%
/ 387
 
5.4%
0 335
 
4.7%
2 322
 
4.5%
5 275
 
3.9%
3 266
 
3.7%
4 204
 
2.9%
6 169
 
2.4%
Other values (4) 422
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18197
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2902
 
15.9%
t 1585
 
8.7%
. 1356
 
7.5%
S 1077
 
5.9%
a 874
 
4.8%
e 693
 
3.8%
o 648
 
3.6%
r 636
 
3.5%
n 628
 
3.5%
l 482
 
2.6%
Other values (54) 7316
40.2%

incident_date
Categorical

Distinct33
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2003-01-07T00:00:00
73 
2003-01-01T00:00:00
68 
2003-01-25T00:00:00
 
67
2003-01-27T00:00:00
 
61
2003-01-03T00:00:00
 
58
Other values (28)
673 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters19000
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row2008-04-01T00:00:00
2nd row2008-04-01T00:00:00
3rd row2008-04-01T00:00:00
4th row2008-04-01T00:00:00
5th row2008-04-01T00:00:00

Common Values

ValueCountFrequency (%)
2003-01-07T00:00:00 73
 
7.3%
2003-01-01T00:00:00 68
 
6.8%
2003-01-25T00:00:00 67
 
6.7%
2003-01-27T00:00:00 61
 
6.1%
2003-01-03T00:00:00 58
 
5.8%
2003-01-09T00:00:00 55
 
5.5%
2003-01-18T00:00:00 54
 
5.4%
2003-01-05T00:00:00 49
 
4.9%
2003-01-20T00:00:00 44
 
4.4%
2003-01-29T00:00:00 43
 
4.3%
Other values (23) 428
42.8%

Length

2023-05-09T22:11:55.111463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2003-01-07t00:00:00 73
 
7.3%
2003-01-01t00:00:00 68
 
6.8%
2003-01-25t00:00:00 67
 
6.7%
2003-01-27t00:00:00 61
 
6.1%
2003-01-03t00:00:00 58
 
5.8%
2003-01-09t00:00:00 55
 
5.5%
2003-01-18t00:00:00 54
 
5.4%
2003-01-05t00:00:00 49
 
4.9%
2003-01-20t00:00:00 44
 
4.4%
2003-01-29t00:00:00 43
 
4.3%
Other values (23) 428
42.8%

Most occurring characters

ValueCountFrequency (%)
0 9486
49.9%
- 2000
 
10.5%
: 2000
 
10.5%
1 1372
 
7.2%
2 1368
 
7.2%
3 1134
 
6.0%
T 1000
 
5.3%
7 134
 
0.7%
5 132
 
0.7%
8 131
 
0.7%
Other values (3) 243
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14000
73.7%
Dash Punctuation 2000
 
10.5%
Other Punctuation 2000
 
10.5%
Uppercase Letter 1000
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9486
67.8%
1 1372
 
9.8%
2 1368
 
9.8%
3 1134
 
8.1%
7 134
 
1.0%
5 132
 
0.9%
8 131
 
0.9%
4 111
 
0.8%
9 100
 
0.7%
6 32
 
0.2%
Dash Punctuation
ValueCountFrequency (%)
- 2000
100.0%
Other Punctuation
ValueCountFrequency (%)
: 2000
100.0%
Uppercase Letter
ValueCountFrequency (%)
T 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18000
94.7%
Latin 1000
 
5.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9486
52.7%
- 2000
 
11.1%
: 2000
 
11.1%
1 1372
 
7.6%
2 1368
 
7.6%
3 1134
 
6.3%
7 134
 
0.7%
5 132
 
0.7%
8 131
 
0.7%
4 111
 
0.6%
Other values (2) 132
 
0.7%
Latin
ValueCountFrequency (%)
T 1000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9486
49.9%
- 2000
 
10.5%
: 2000
 
10.5%
1 1372
 
7.2%
2 1368
 
7.2%
3 1134
 
6.0%
T 1000
 
5.3%
7 134
 
0.7%
5 132
 
0.7%
8 131
 
0.7%
Other values (3) 243
 
1.3%

call_number
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33350792
Minimum30010002
Maximum80940325
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-05-09T22:11:55.287125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum30010002
5-th percentile30010406
Q130070363
median30160253
Q330250361
95-th percentile80920085
Maximum80940325
Range50930323
Interquartile range (IQR)179997.75

Descriptive statistics

Standard deviation12342769
Coefficient of variation (CV)0.37008924
Kurtosis10.999645
Mean33350792
Median Absolute Deviation (MAD)90041
Skewness3.6023198
Sum3.3350792 × 1010
Variance1.5234395 × 1014
MonotonicityNot monotonic
2023-05-09T22:11:55.489998image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80920257 1
 
0.1%
30070400 1
 
0.1%
30050155 1
 
0.1%
30050159 1
 
0.1%
30050160 1
 
0.1%
30050170 1
 
0.1%
30050172 1
 
0.1%
30050176 1
 
0.1%
30070386 1
 
0.1%
30070389 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
30010002 1
0.1%
30010005 1
0.1%
30010007 1
0.1%
30010016 1
0.1%
30010019 1
0.1%
30010025 1
0.1%
30010028 1
0.1%
30010036 1
0.1%
30010039 1
0.1%
30010056 1
0.1%
ValueCountFrequency (%)
80940325 1
0.1%
80940318 1
0.1%
80940315 1
0.1%
80940310 1
0.1%
80940309 1
0.1%
80940305 1
0.1%
80940295 1
0.1%
80940287 1
0.1%
80940271 1
0.1%
80940245 1
0.1%

alarm_dttm
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2008-04-01T18:06:37
 
1
2003-01-07T19:44:35
 
1
2003-01-05T11:44:46
 
1
2003-01-05T11:58:20
 
1
2003-01-05T11:59:28
 
1
Other values (995)
995 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters19000
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1000 ?
Unique (%)100.0%

Sample

1st row2008-04-01T18:06:37
2nd row2008-04-01T18:00:52
3rd row2008-04-01T18:42:06
4th row2008-04-01T19:03:52
5th row2008-04-01T19:16:12

Common Values

ValueCountFrequency (%)
2008-04-01T18:06:37 1
 
0.1%
2003-01-07T19:44:35 1
 
0.1%
2003-01-05T11:44:46 1
 
0.1%
2003-01-05T11:58:20 1
 
0.1%
2003-01-05T11:59:28 1
 
0.1%
2003-01-05T12:18:39 1
 
0.1%
2003-01-05T12:33:47 1
 
0.1%
2003-01-05T12:57:08 1
 
0.1%
2003-01-07T19:06:49 1
 
0.1%
2003-01-07T19:20:13 1
 
0.1%
Other values (990) 990
99.0%

Length

2023-05-09T22:11:55.660700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2008-04-01t18:06:37 1
 
0.1%
2008-04-01t00:34:39 1
 
0.1%
2008-04-01t11:09:17 1
 
0.1%
2008-04-01t11:09:26 1
 
0.1%
2008-04-01t18:42:06 1
 
0.1%
2008-04-01t19:03:52 1
 
0.1%
2008-04-01t19:16:12 1
 
0.1%
2008-04-01t20:25:00 1
 
0.1%
2008-04-01t20:09:55 1
 
0.1%
2008-04-01t21:16:25 1
 
0.1%
Other values (990) 990
99.0%

Most occurring characters

ValueCountFrequency (%)
0 4375
23.0%
1 2612
13.7%
2 2162
11.4%
- 2000
10.5%
: 2000
10.5%
3 1768
9.3%
T 1000
 
5.3%
5 773
 
4.1%
4 761
 
4.0%
7 433
 
2.3%
Other values (3) 1116
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14000
73.7%
Dash Punctuation 2000
 
10.5%
Other Punctuation 2000
 
10.5%
Uppercase Letter 1000
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4375
31.2%
1 2612
18.7%
2 2162
15.4%
3 1768
12.6%
5 773
 
5.5%
4 761
 
5.4%
7 433
 
3.1%
8 410
 
2.9%
9 386
 
2.8%
6 320
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 2000
100.0%
Other Punctuation
ValueCountFrequency (%)
: 2000
100.0%
Uppercase Letter
ValueCountFrequency (%)
T 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18000
94.7%
Latin 1000
 
5.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4375
24.3%
1 2612
14.5%
2 2162
12.0%
- 2000
11.1%
: 2000
11.1%
3 1768
9.8%
5 773
 
4.3%
4 761
 
4.2%
7 433
 
2.4%
8 410
 
2.3%
Other values (2) 706
 
3.9%
Latin
ValueCountFrequency (%)
T 1000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4375
23.0%
1 2612
13.7%
2 2162
11.4%
- 2000
10.5%
: 2000
10.5%
3 1768
9.3%
T 1000
 
5.3%
5 773
 
4.1%
4 761
 
4.0%
7 433
 
2.3%
Other values (3) 1116
 
5.9%

arrival_dttm
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct997
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2003-01-20T15:16:49
 
2
2003-01-16T15:39:32
 
2
2003-01-20T15:29:53
 
2
2008-04-01T18:15:19
 
1
2003-01-07T19:31:42
 
1
Other values (992)
992 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters19000
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique994 ?
Unique (%)99.4%

Sample

1st row2008-04-01T18:15:19
2nd row2008-04-01T18:06:30
3rd row2008-04-01T18:45:23
4th row2008-04-01T19:08:39
5th row2008-04-01T19:23:48

Common Values

ValueCountFrequency (%)
2003-01-20T15:16:49 2
 
0.2%
2003-01-16T15:39:32 2
 
0.2%
2003-01-20T15:29:53 2
 
0.2%
2008-04-01T18:15:19 1
 
0.1%
2003-01-07T19:31:42 1
 
0.1%
2003-01-07T19:47:21 1
 
0.1%
2003-01-07T19:46:47 1
 
0.1%
2003-01-07T08:29:04 1
 
0.1%
2003-01-07T07:27:27 1
 
0.1%
2003-01-07T19:30:25 1
 
0.1%
Other values (987) 987
98.7%

Length

2023-05-09T22:11:55.814856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2003-01-20t15:16:49 2
 
0.2%
2003-01-20t15:29:53 2
 
0.2%
2003-01-16t15:39:32 2
 
0.2%
2003-01-25t01:15:36 1
 
0.1%
2008-04-01t00:28:41 1
 
0.1%
2008-04-01t11:21:08 1
 
0.1%
2008-04-02t16:52:52 1
 
0.1%
2008-04-01t11:15:27 1
 
0.1%
2008-04-01t18:45:23 1
 
0.1%
2008-04-01t19:08:39 1
 
0.1%
Other values (987) 987
98.7%

Most occurring characters

ValueCountFrequency (%)
0 4349
22.9%
1 2586
13.6%
2 2185
11.5%
- 2000
10.5%
: 2000
10.5%
3 1760
9.3%
T 1000
 
5.3%
5 787
 
4.1%
4 709
 
3.7%
8 442
 
2.3%
Other values (3) 1182
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14000
73.7%
Dash Punctuation 2000
 
10.5%
Other Punctuation 2000
 
10.5%
Uppercase Letter 1000
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4349
31.1%
1 2586
18.5%
2 2185
15.6%
3 1760
12.6%
5 787
 
5.6%
4 709
 
5.1%
8 442
 
3.2%
7 433
 
3.1%
9 400
 
2.9%
6 349
 
2.5%
Dash Punctuation
ValueCountFrequency (%)
- 2000
100.0%
Other Punctuation
ValueCountFrequency (%)
: 2000
100.0%
Uppercase Letter
ValueCountFrequency (%)
T 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18000
94.7%
Latin 1000
 
5.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4349
24.2%
1 2586
14.4%
2 2185
12.1%
- 2000
11.1%
: 2000
11.1%
3 1760
9.8%
5 787
 
4.4%
4 709
 
3.9%
8 442
 
2.5%
7 433
 
2.4%
Other values (2) 749
 
4.2%
Latin
ValueCountFrequency (%)
T 1000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4349
22.9%
1 2586
13.6%
2 2185
11.5%
- 2000
10.5%
: 2000
10.5%
3 1760
9.3%
T 1000
 
5.3%
5 787
 
4.1%
4 709
 
3.7%
8 442
 
2.3%
Other values (3) 1182
 
6.2%

close_dttm
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct998
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2003-01-27T06:34:35
 
2
2003-01-25T12:31:41
 
2
2008-04-01T18:21:48
 
1
2003-01-05T11:27:28
 
1
2003-01-05T11:45:21
 
1
Other values (993)
993 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters19000
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique996 ?
Unique (%)99.6%

Sample

1st row2008-04-01T18:21:48
2nd row2008-04-01T18:22:18
3rd row2008-04-01T18:53:25
4th row2008-04-01T19:35:36
5th row2008-04-01T19:28:49

Common Values

ValueCountFrequency (%)
2003-01-27T06:34:35 2
 
0.2%
2003-01-25T12:31:41 2
 
0.2%
2008-04-01T18:21:48 1
 
0.1%
2003-01-05T11:27:28 1
 
0.1%
2003-01-05T11:45:21 1
 
0.1%
2003-01-05T12:01:44 1
 
0.1%
2003-01-05T12:32:57 1
 
0.1%
2003-01-05T12:27:28 1
 
0.1%
2003-01-05T12:48:56 1
 
0.1%
2003-01-05T13:04:00 1
 
0.1%
Other values (988) 988
98.8%

Length

2023-05-09T22:11:55.952761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2003-01-27t06:34:35 2
 
0.2%
2003-01-25t12:31:41 2
 
0.2%
2003-01-15t13:49:16 1
 
0.1%
2008-04-01t02:08:13 1
 
0.1%
2008-04-01t11:32:18 1
 
0.1%
2008-04-01t18:53:25 1
 
0.1%
2008-04-01t19:35:36 1
 
0.1%
2008-04-01t19:28:49 1
 
0.1%
2008-04-01t20:51:22 1
 
0.1%
2008-04-01t20:13:09 1
 
0.1%
Other values (988) 988
98.8%

Most occurring characters

ValueCountFrequency (%)
0 4411
23.2%
1 2609
13.7%
2 2147
11.3%
- 2000
10.5%
: 2000
10.5%
3 1776
9.3%
T 1000
 
5.3%
5 755
 
4.0%
4 713
 
3.8%
8 444
 
2.3%
Other values (3) 1145
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14000
73.7%
Dash Punctuation 2000
 
10.5%
Other Punctuation 2000
 
10.5%
Uppercase Letter 1000
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4411
31.5%
1 2609
18.6%
2 2147
15.3%
3 1776
12.7%
5 755
 
5.4%
4 713
 
5.1%
8 444
 
3.2%
7 425
 
3.0%
9 384
 
2.7%
6 336
 
2.4%
Dash Punctuation
ValueCountFrequency (%)
- 2000
100.0%
Other Punctuation
ValueCountFrequency (%)
: 2000
100.0%
Uppercase Letter
ValueCountFrequency (%)
T 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18000
94.7%
Latin 1000
 
5.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4411
24.5%
1 2609
14.5%
2 2147
11.9%
- 2000
11.1%
: 2000
11.1%
3 1776
9.9%
5 755
 
4.2%
4 713
 
4.0%
8 444
 
2.5%
7 425
 
2.4%
Other values (2) 720
 
4.0%
Latin
ValueCountFrequency (%)
T 1000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4411
23.2%
1 2609
13.7%
2 2147
11.3%
- 2000
10.5%
: 2000
10.5%
3 1776
9.3%
T 1000
 
5.3%
5 755
 
4.0%
4 713
 
3.8%
8 444
 
2.3%
Other values (3) 1145
 
6.0%

city
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
SF
993 
SFO
 
3
TI
 
3
FM
 
1

Length

Max length3
Median length2
Mean length2.003
Min length2

Characters and Unicode

Total characters2003
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowSF
2nd rowSF
3rd rowSF
4th rowSF
5th rowSF

Common Values

ValueCountFrequency (%)
SF 993
99.3%
SFO 3
 
0.3%
TI 3
 
0.3%
FM 1
 
0.1%

Length

2023-05-09T22:11:56.103965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T22:11:56.273878image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
sf 993
99.3%
sfo 3
 
0.3%
ti 3
 
0.3%
fm 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
F 997
49.8%
S 996
49.7%
O 3
 
0.1%
T 3
 
0.1%
I 3
 
0.1%
M 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2003
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 997
49.8%
S 996
49.7%
O 3
 
0.1%
T 3
 
0.1%
I 3
 
0.1%
M 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 2003
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 997
49.8%
S 996
49.7%
O 3
 
0.1%
T 3
 
0.1%
I 3
 
0.1%
M 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2003
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 997
49.8%
S 996
49.7%
O 3
 
0.1%
T 3
 
0.1%
I 3
 
0.1%
M 1
 
< 0.1%

zipcode
Real number (ℝ)

Distinct27
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94114.32
Minimum94102
Maximum94134
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-05-09T22:11:56.409217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum94102
5-th percentile94102
Q194108
median94114
Q394121
95-th percentile94133
Maximum94134
Range32
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.8517769
Coefficient of variation (CV)9.4053454 × 10-5
Kurtosis-0.49387477
Mean94114.32
Median Absolute Deviation (MAD)7
Skewness0.57218657
Sum94114320
Variance78.353954
MonotonicityNot monotonic
2023-05-09T22:11:56.567719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
94110 88
 
8.8%
94109 81
 
8.1%
94105 75
 
7.5%
94115 73
 
7.3%
94124 72
 
7.2%
94102 70
 
7.0%
94117 55
 
5.5%
94112 53
 
5.3%
94114 51
 
5.1%
94122 37
 
3.7%
Other values (17) 345
34.5%
ValueCountFrequency (%)
94102 70
7.0%
94103 34
 
3.4%
94104 36
3.6%
94105 75
7.5%
94107 32
 
3.2%
94108 10
 
1.0%
94109 81
8.1%
94110 88
8.8%
94111 18
 
1.8%
94112 53
5.3%
ValueCountFrequency (%)
94134 31
3.1%
94133 28
 
2.8%
94132 11
 
1.1%
94131 16
 
1.6%
94130 3
 
0.3%
94129 1
 
0.1%
94128 2
 
0.2%
94127 12
 
1.2%
94124 72
7.2%
94123 28
 
2.8%

battalion
Categorical

Distinct11
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
B02
147 
B03
128 
B04
118 
B01
101 
B10
99 
Other values (6)
407 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3000
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowB06
2nd rowB10
3rd rowB03
4th rowB02
5th rowB07

Common Values

ValueCountFrequency (%)
B02 147
14.7%
B03 128
12.8%
B04 118
11.8%
B01 101
10.1%
B10 99
9.9%
B05 94
9.4%
B08 90
9.0%
B09 85
8.5%
B06 74
7.4%
B07 63
6.3%

Length

2023-05-09T22:11:56.735897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b02 147
14.7%
b03 128
12.8%
b04 118
11.8%
b01 101
10.1%
b10 99
9.9%
b05 94
9.4%
b08 90
9.0%
b09 85
8.5%
b06 74
7.4%
b07 63
6.3%

Most occurring characters

ValueCountFrequency (%)
B 1000
33.3%
0 999
33.3%
1 200
 
6.7%
2 147
 
4.9%
3 128
 
4.3%
4 118
 
3.9%
5 94
 
3.1%
8 90
 
3.0%
9 87
 
2.9%
6 74
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
66.7%
Uppercase Letter 1000
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 999
50.0%
1 200
 
10.0%
2 147
 
7.3%
3 128
 
6.4%
4 118
 
5.9%
5 94
 
4.7%
8 90
 
4.5%
9 87
 
4.3%
6 74
 
3.7%
7 63
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
B 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
66.7%
Latin 1000
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 999
50.0%
1 200
 
10.0%
2 147
 
7.3%
3 128
 
6.4%
4 118
 
5.9%
5 94
 
4.7%
8 90
 
4.5%
9 87
 
4.3%
6 74
 
3.7%
7 63
 
3.1%
Latin
ValueCountFrequency (%)
B 1000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 1000
33.3%
0 999
33.3%
1 200
 
6.7%
2 147
 
4.9%
3 128
 
4.3%
4 118
 
3.9%
5 94
 
3.1%
8 90
 
3.0%
9 87
 
2.9%
6 74
 
2.5%

station_area
Categorical

Distinct43
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
01
87 
36
 
53
13
 
53
03
 
49
07
 
43
Other values (38)
715 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2000
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11
2nd row37
3rd row01
4th row36
5th row14

Common Values

ValueCountFrequency (%)
01 87
 
8.7%
36 53
 
5.3%
13 53
 
5.3%
03 49
 
4.9%
07 43
 
4.3%
05 36
 
3.6%
06 36
 
3.6%
33 35
 
3.5%
16 34
 
3.4%
10 34
 
3.4%
Other values (33) 540
54.0%

Length

2023-05-09T22:11:56.887602image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
01 87
 
8.7%
36 53
 
5.3%
13 53
 
5.3%
03 49
 
4.9%
07 43
 
4.3%
05 36
 
3.6%
06 36
 
3.6%
33 35
 
3.5%
16 34
 
3.4%
10 34
 
3.4%
Other values (33) 540
54.0%

Most occurring characters

ValueCountFrequency (%)
1 449
22.4%
3 372
18.6%
0 354
17.7%
2 211
10.5%
4 150
 
7.5%
6 128
 
6.4%
8 95
 
4.8%
7 86
 
4.3%
5 79
 
4.0%
9 74
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1998
99.9%
Uppercase Letter 2
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 449
22.5%
3 372
18.6%
0 354
17.7%
2 211
10.6%
4 150
 
7.5%
6 128
 
6.4%
8 95
 
4.8%
7 86
 
4.3%
5 79
 
4.0%
9 74
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
A 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1998
99.9%
Latin 2
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 449
22.5%
3 372
18.6%
0 354
17.7%
2 211
10.6%
4 150
 
7.5%
6 128
 
6.4%
8 95
 
4.8%
7 86
 
4.3%
5 79
 
4.0%
9 74
 
3.7%
Latin
ValueCountFrequency (%)
A 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 449
22.4%
3 372
18.6%
0 354
17.7%
2 211
10.5%
4 150
 
7.5%
6 128
 
6.4%
8 95
 
4.8%
7 86
 
4.3%
5 79
 
4.0%
9 74
 
3.7%

suppression_units
Real number (ℝ)

Distinct13
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.938
Minimum0
Maximum38
Zeros4
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-05-09T22:11:57.052226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q33
95-th percentile3
Maximum38
Range38
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7490612
Coefficient of variation (CV)0.90250838
Kurtosis184.04735
Mean1.938
Median Absolute Deviation (MAD)0
Skewness10.000291
Sum1938
Variance3.0592152
MonotonicityNot monotonic
2023-05-09T22:11:57.542452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 504
50.4%
3 232
23.2%
2 220
22.0%
5 11
 
1.1%
7 10
 
1.0%
4 5
 
0.5%
10 4
 
0.4%
8 4
 
0.4%
0 4
 
0.4%
11 2
 
0.2%
Other values (3) 4
 
0.4%
ValueCountFrequency (%)
0 4
 
0.4%
1 504
50.4%
2 220
22.0%
3 232
23.2%
4 5
 
0.5%
5 11
 
1.1%
6 2
 
0.2%
7 10
 
1.0%
8 4
 
0.4%
9 1
 
0.1%
ValueCountFrequency (%)
38 1
 
0.1%
11 2
 
0.2%
10 4
 
0.4%
9 1
 
0.1%
8 4
 
0.4%
7 10
 
1.0%
6 2
 
0.2%
5 11
 
1.1%
4 5
 
0.5%
3 232
23.2%

suppression_personnel
Real number (ℝ)

Distinct29
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.451
Minimum0
Maximum50
Zeros4
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-05-09T22:11:57.997079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q14
median5
Q310
95-th percentile12
Maximum50
Range50
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.1271714
Coefficient of variation (CV)0.68811857
Kurtosis16.231972
Mean7.451
Median Absolute Deviation (MAD)2
Skewness3.1083511
Sum7451
Variance26.287887
MonotonicityNot monotonic
2023-05-09T22:11:58.573807image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
4 418
41.8%
9 202
20.2%
11 195
19.5%
5 61
 
6.1%
1 18
 
1.8%
12 16
 
1.6%
6 15
 
1.5%
10 14
 
1.4%
3 8
 
0.8%
30 6
 
0.6%
Other values (19) 47
 
4.7%
ValueCountFrequency (%)
0 4
 
0.4%
1 18
 
1.8%
2 5
 
0.5%
3 8
 
0.8%
4 418
41.8%
5 61
 
6.1%
6 15
 
1.5%
7 3
 
0.3%
8 4
 
0.4%
9 202
20.2%
ValueCountFrequency (%)
50 2
 
0.2%
36 1
 
0.1%
35 2
 
0.2%
34 1
 
0.1%
33 3
0.3%
32 1
 
0.1%
30 6
0.6%
26 5
0.5%
25 1
 
0.1%
24 1
 
0.1%

ems_units
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
955 
1
 
36
2
 
6
3
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 955
95.5%
1 36
 
3.6%
2 6
 
0.6%
3 3
 
0.3%

Length

2023-05-09T22:11:59.090714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T22:11:59.469853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 955
95.5%
1 36
 
3.6%
2 6
 
0.6%
3 3
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 955
95.5%
1 36
 
3.6%
2 6
 
0.6%
3 3
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 955
95.5%
1 36
 
3.6%
2 6
 
0.6%
3 3
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 955
95.5%
1 36
 
3.6%
2 6
 
0.6%
3 3
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 955
95.5%
1 36
 
3.6%
2 6
 
0.6%
3 3
 
0.3%

ems_personnel
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.104
Minimum0
Maximum11
Zeros955
Zeros (%)95.5%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-05-09T22:11:59.634959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.58611221
Coefficient of variation (CV)5.6356944
Kurtosis135.56103
Mean0.104
Median Absolute Deviation (MAD)0
Skewness9.5435997
Sum104
Variance0.34352753
MonotonicityNot monotonic
2023-05-09T22:11:59.780398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 955
95.5%
2 26
 
2.6%
1 9
 
0.9%
3 6
 
0.6%
5 2
 
0.2%
11 1
 
0.1%
4 1
 
0.1%
ValueCountFrequency (%)
0 955
95.5%
1 9
 
0.9%
2 26
 
2.6%
3 6
 
0.6%
4 1
 
0.1%
5 2
 
0.2%
11 1
 
0.1%
ValueCountFrequency (%)
11 1
 
0.1%
5 2
 
0.2%
4 1
 
0.1%
3 6
 
0.6%
2 26
 
2.6%
1 9
 
0.9%
0 955
95.5%

other_units
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
979 
1
 
19
4
 
1
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 979
97.9%
1 19
 
1.9%
4 1
 
0.1%
2 1
 
0.1%

Length

2023-05-09T22:11:59.931640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T22:12:00.084337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 979
97.9%
1 19
 
1.9%
4 1
 
0.1%
2 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 979
97.9%
1 19
 
1.9%
4 1
 
0.1%
2 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 979
97.9%
1 19
 
1.9%
4 1
 
0.1%
2 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 979
97.9%
1 19
 
1.9%
4 1
 
0.1%
2 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 979
97.9%
1 19
 
1.9%
4 1
 
0.1%
2 1
 
0.1%

other_personnel
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
979 
1
 
11
2
 
9
10
 
1

Length

Max length2
Median length1
Mean length1.001
Min length1

Characters and Unicode

Total characters1001
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 979
97.9%
1 11
 
1.1%
2 9
 
0.9%
10 1
 
0.1%

Length

2023-05-09T22:12:00.215304image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T22:12:00.366847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 979
97.9%
1 11
 
1.1%
2 9
 
0.9%
10 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 980
97.9%
1 12
 
1.2%
2 9
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 980
97.9%
1 12
 
1.2%
2 9
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 980
97.9%
1 12
 
1.2%
2 9
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 980
97.9%
1 12
 
1.2%
2 9
 
0.9%

first_unit_on_scene
Categorical

HIGH CARDINALITY  HIGH CORRELATION  MISSING 

Distinct81
Distinct (%)9.2%
Missing121
Missing (%)12.1%
Memory size7.9 KiB
E03
 
46
E36
 
43
E13
 
37
E06
 
34
E01
 
33
Other values (76)
686 

Length

Max length3
Median length3
Mean length2.9954494
Min length2

Characters and Unicode

Total characters2633
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)1.4%

Sample

1st rowE11
2nd rowE37
3rd rowE01
4th rowE36
5th rowE14

Common Values

ValueCountFrequency (%)
E03 46
 
4.6%
E36 43
 
4.3%
E13 37
 
3.7%
E06 34
 
3.4%
E01 33
 
3.3%
E38 32
 
3.2%
E05 29
 
2.9%
E07 28
 
2.8%
E16 28
 
2.8%
E29 27
 
2.7%
Other values (71) 542
54.2%
(Missing) 121
 
12.1%

Length

2023-05-09T22:12:00.502565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
e03 46
 
5.2%
e36 43
 
4.9%
e13 37
 
4.2%
e06 34
 
3.9%
e01 33
 
3.8%
e38 32
 
3.6%
e05 29
 
3.3%
e07 28
 
3.2%
e16 28
 
3.2%
e29 27
 
3.1%
Other values (71) 542
61.7%

Most occurring characters

ValueCountFrequency (%)
E 757
28.8%
1 359
13.6%
3 321
12.2%
0 307
11.7%
2 200
 
7.6%
4 131
 
5.0%
6 117
 
4.4%
8 93
 
3.5%
T 87
 
3.3%
7 78
 
3.0%
Other values (9) 183
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1743
66.2%
Uppercase Letter 890
33.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 359
20.6%
3 321
18.4%
0 307
17.6%
2 200
11.5%
4 131
 
7.5%
6 117
 
6.7%
8 93
 
5.3%
7 78
 
4.5%
5 71
 
4.1%
9 66
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
E 757
85.1%
T 87
 
9.8%
B 11
 
1.2%
R 11
 
1.2%
M 9
 
1.0%
C 7
 
0.8%
A 3
 
0.3%
S 3
 
0.3%
P 2
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1743
66.2%
Latin 890
33.8%

Most frequent character per script

Common
ValueCountFrequency (%)
1 359
20.6%
3 321
18.4%
0 307
17.6%
2 200
11.5%
4 131
 
7.5%
6 117
 
6.7%
8 93
 
5.3%
7 78
 
4.5%
5 71
 
4.1%
9 66
 
3.8%
Latin
ValueCountFrequency (%)
E 757
85.1%
T 87
 
9.8%
B 11
 
1.2%
R 11
 
1.2%
M 9
 
1.0%
C 7
 
0.8%
A 3
 
0.3%
S 3
 
0.3%
P 2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2633
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 757
28.8%
1 359
13.6%
3 321
12.2%
0 307
11.7%
2 200
 
7.6%
4 131
 
5.0%
6 117
 
4.4%
8 93
 
3.5%
T 87
 
3.3%
7 78
 
3.0%
Other values (9) 183
 
7.0%

fire_fatalities
Categorical

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
1000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1000
100.0%

Length

2023-05-09T22:12:00.646677image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T22:12:00.780592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1000
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1000
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1000
100.0%

fire_injuries
Categorical

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
1000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1000
100.0%

Length

2023-05-09T22:12:00.894795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T22:12:01.030504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1000
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1000
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1000
100.0%
Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
1000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1000
100.0%

Length

2023-05-09T22:12:01.146753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T22:12:01.286530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1000
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1000
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1000
100.0%
Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
1000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1000
100.0%

Length

2023-05-09T22:12:01.401855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T22:12:01.538625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1000
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1000
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1000
100.0%

number_of_alarms
Categorical

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
1000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1000
100.0%

Length

2023-05-09T22:12:01.656351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T22:12:01.793267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1000
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1000
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1000
100.0%

primary_situation
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct85
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
711 - Municipal alarm system, Street Box False
237 
700 - False alarm or false call, other
70 
730 - System malfunction, other
 
47
740 - Unintentional alarm, other
 
39
745 - Alarm system sounded/no fire-accidental
 
36
Other values (80)
571 

Length

Max length48
Median length45
Mean length37.631
Min length14

Characters and Unicode

Total characters37631
Distinct characters63
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)2.7%

Sample

1st row412 - Gas leak (natural gas or LPG)
2nd row552 - Police matter
3rd row210 - Steam Rupture, steam, other
4th row522 - Water or steam leak
5th row520 - Water problem, other

Common Values

ValueCountFrequency (%)
711 - Municipal alarm system, Street Box False 237
23.7%
700 - False alarm or false call, other 70
 
7.0%
730 - System malfunction, other 47
 
4.7%
740 - Unintentional alarm, other 39
 
3.9%
745 - Alarm system sounded/no fire-accidental 36
 
3.6%
118 - Trash or rubbish fire, contained 34
 
3.4%
311 - Medical assist, assist EMS crew 32
 
3.2%
735 - Alarm system sounded due to malfunction 31
 
3.1%
500 - Service Call, other 28
 
2.8%
151 - Outside rubbish, trash or waste fire 26
 
2.6%
Other values (75) 420
42.0%

Length

2023-05-09T22:12:01.941634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1024
 
15.2%
alarm 435
 
6.5%
false 400
 
5.9%
system 351
 
5.2%
other 325
 
4.8%
711 237
 
3.5%
municipal 237
 
3.5%
street 237
 
3.5%
box 237
 
3.5%
or 214
 
3.2%
Other values (257) 3027
45.0%

Most occurring characters

ValueCountFrequency (%)
5724
15.2%
e 3021
 
8.0%
a 2579
 
6.9%
t 2151
 
5.7%
r 1974
 
5.2%
l 1962
 
5.2%
s 1952
 
5.2%
i 1851
 
4.9%
o 1775
 
4.7%
n 1276
 
3.4%
Other values (53) 13366
35.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 24880
66.1%
Space Separator 5724
 
15.2%
Decimal Number 3018
 
8.0%
Uppercase Letter 1970
 
5.2%
Dash Punctuation 1066
 
2.8%
Other Punctuation 893
 
2.4%
Close Punctuation 40
 
0.1%
Open Punctuation 40
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3021
12.1%
a 2579
10.4%
t 2151
8.6%
r 1974
 
7.9%
l 1962
 
7.9%
s 1952
 
7.8%
i 1851
 
7.4%
o 1775
 
7.1%
n 1276
 
5.1%
m 1158
 
4.7%
Other values (16) 5181
20.8%
Uppercase Letter
ValueCountFrequency (%)
S 438
22.2%
F 333
16.9%
M 329
16.7%
B 255
12.9%
A 126
 
6.4%
C 55
 
2.8%
G 54
 
2.7%
P 51
 
2.6%
E 49
 
2.5%
W 44
 
2.2%
Other values (9) 236
12.0%
Decimal Number
ValueCountFrequency (%)
1 963
31.9%
7 536
17.8%
0 437
14.5%
5 334
 
11.1%
3 285
 
9.4%
4 234
 
7.8%
2 130
 
4.3%
6 56
 
1.9%
8 35
 
1.2%
9 8
 
0.3%
Other Punctuation
ValueCountFrequency (%)
, 757
84.8%
/ 89
 
10.0%
. 44
 
4.9%
& 3
 
0.3%
Space Separator
ValueCountFrequency (%)
5724
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1066
100.0%
Close Punctuation
ValueCountFrequency (%)
) 40
100.0%
Open Punctuation
ValueCountFrequency (%)
( 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 26850
71.4%
Common 10781
28.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3021
11.3%
a 2579
 
9.6%
t 2151
 
8.0%
r 1974
 
7.4%
l 1962
 
7.3%
s 1952
 
7.3%
i 1851
 
6.9%
o 1775
 
6.6%
n 1276
 
4.8%
m 1158
 
4.3%
Other values (35) 7151
26.6%
Common
ValueCountFrequency (%)
5724
53.1%
- 1066
 
9.9%
1 963
 
8.9%
, 757
 
7.0%
7 536
 
5.0%
0 437
 
4.1%
5 334
 
3.1%
3 285
 
2.6%
4 234
 
2.2%
2 130
 
1.2%
Other values (8) 315
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37631
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5724
15.2%
e 3021
 
8.0%
a 2579
 
6.9%
t 2151
 
5.7%
r 1974
 
5.2%
l 1962
 
5.2%
s 1952
 
5.2%
i 1851
 
4.9%
o 1775
 
4.7%
n 1276
 
3.4%
Other values (53) 13366
35.5%

mutual_aid
Categorical

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
None
1000 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4000
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone
2nd rowNone
3rd rowNone
4th rowNone
5th rowNone

Common Values

ValueCountFrequency (%)
None 1000
100.0%

Length

2023-05-09T22:12:02.117972image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T22:12:02.264175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
none 1000
100.0%

Most occurring characters

ValueCountFrequency (%)
N 1000
25.0%
o 1000
25.0%
n 1000
25.0%
e 1000
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3000
75.0%
Uppercase Letter 1000
 
25.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1000
33.3%
n 1000
33.3%
e 1000
33.3%
Uppercase Letter
ValueCountFrequency (%)
N 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 1000
25.0%
o 1000
25.0%
n 1000
25.0%
e 1000
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 1000
25.0%
o 1000
25.0%
n 1000
25.0%
e 1000
25.0%

action_taken_primary
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct42
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
86 - Investigate
684 
11 - Extinguish
 
53
63 - Restore fire alarm system
 
26
45 - Remove hazard
 
22
71 - Assist physically disabled
 
18
Other values (37)
197 

Length

Max length45
Median length16
Mean length18.575
Min length5

Characters and Unicode

Total characters18575
Distinct characters59
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)1.1%

Sample

1st row86 - Investigate
2nd row76 - Provide water
3rd row86 - Investigate
4th row64 - Shut down system
5th row00 - Action taken, other

Common Values

ValueCountFrequency (%)
86 - Investigate 684
68.4%
11 - Extinguish 53
 
5.3%
63 - Restore fire alarm system 26
 
2.6%
45 - Remove hazard 22
 
2.2%
71 - Assist physically disabled 18
 
1.8%
00 - Action taken, other 16
 
1.6%
64 - Shut down system 16
 
1.6%
70 - Assistance, other 16
 
1.6%
73 - Provide manpower 15
 
1.5%
52 - Forcible entry 14
 
1.4%
Other values (32) 120
 
12.0%

Length

2023-05-09T22:12:02.421965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1015
28.8%
investigate 686
19.4%
86 684
19.4%
other 72
 
2.0%
11 53
 
1.5%
extinguish 53
 
1.5%
system 48
 
1.4%
fire 47
 
1.3%
provide 40
 
1.1%
restore 34
 
1.0%
Other values (117) 795
22.5%

Most occurring characters

ValueCountFrequency (%)
2527
13.6%
e 2039
 
11.0%
t 1795
 
9.7%
i 1125
 
6.1%
s 1124
 
6.1%
a 1018
 
5.5%
- 1002
 
5.4%
n 955
 
5.1%
v 788
 
4.2%
g 760
 
4.1%
Other values (49) 5442
29.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11872
63.9%
Space Separator 2527
 
13.6%
Decimal Number 2001
 
10.8%
Uppercase Letter 1042
 
5.6%
Dash Punctuation 1002
 
5.4%
Other Punctuation 105
 
0.6%
Open Punctuation 13
 
0.1%
Close Punctuation 13
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2039
17.2%
t 1795
15.1%
i 1125
9.5%
s 1124
9.5%
a 1018
8.6%
n 955
8.0%
v 788
 
6.6%
g 760
 
6.4%
r 421
 
3.5%
o 338
 
2.8%
Other values (16) 1509
12.7%
Uppercase Letter
ValueCountFrequency (%)
I 692
66.4%
E 75
 
7.2%
R 67
 
6.4%
A 57
 
5.5%
P 40
 
3.8%
S 35
 
3.4%
F 29
 
2.8%
L 13
 
1.2%
B 7
 
0.7%
H 7
 
0.7%
Other values (5) 20
 
1.9%
Decimal Number
ValueCountFrequency (%)
6 748
37.4%
8 697
34.8%
1 156
 
7.8%
3 89
 
4.4%
0 83
 
4.1%
7 59
 
2.9%
5 58
 
2.9%
2 53
 
2.6%
4 50
 
2.5%
9 8
 
0.4%
Other Punctuation
ValueCountFrequency (%)
, 79
75.2%
& 13
 
12.4%
. 9
 
8.6%
/ 4
 
3.8%
Space Separator
ValueCountFrequency (%)
2527
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1002
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12914
69.5%
Common 5661
30.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2039
15.8%
t 1795
13.9%
i 1125
8.7%
s 1124
8.7%
a 1018
7.9%
n 955
7.4%
v 788
 
6.1%
g 760
 
5.9%
I 692
 
5.4%
r 421
 
3.3%
Other values (31) 2197
17.0%
Common
ValueCountFrequency (%)
2527
44.6%
- 1002
 
17.7%
6 748
 
13.2%
8 697
 
12.3%
1 156
 
2.8%
3 89
 
1.6%
0 83
 
1.5%
, 79
 
1.4%
7 59
 
1.0%
5 58
 
1.0%
Other values (8) 163
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18575
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2527
13.6%
e 2039
 
11.0%
t 1795
 
9.7%
i 1125
 
6.1%
s 1124
 
6.1%
a 1018
 
5.5%
- 1002
 
5.4%
n 955
 
5.1%
v 788
 
4.2%
g 760
 
4.1%
Other values (49) 5442
29.3%

action_taken_secondary
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
-
977 
86 - Investigate
 
14
12 - Salvage & overhaul
 
3
63 - Restore fire alarm system
 
2
34 - Transport person
 
1
Other values (3)
 
3

Length

Max length41
Median length1
Mean length1.439
Min length1

Characters and Unicode

Total characters1439
Distinct characters39
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.4%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 977
97.7%
86 - Investigate 14
 
1.4%
12 - Salvage & overhaul 3
 
0.3%
63 - Restore fire alarm system 2
 
0.2%
34 - Transport person 1
 
0.1%
82 - Notify other agencies. 1
 
0.1%
43 - HazMat spill control and confinement 1
 
0.1%
78 - Control traffic 1
 
0.1%

Length

2023-05-09T22:12:02.638047image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T22:12:02.846696image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1003
94.1%
investigate 14
 
1.3%
86 14
 
1.3%
12 3
 
0.3%
salvage 3
 
0.3%
overhaul 3
 
0.3%
system 2
 
0.2%
control 2
 
0.2%
alarm 2
 
0.2%
fire 2
 
0.2%
Other values (16) 18
 
1.7%

Most occurring characters

ValueCountFrequency (%)
- 1000
69.5%
66
 
4.6%
e 48
 
3.3%
t 40
 
2.8%
a 33
 
2.3%
s 24
 
1.7%
n 23
 
1.6%
i 21
 
1.5%
v 20
 
1.4%
g 18
 
1.3%
Other values (29) 146
 
10.1%

Most occurring categories

ValueCountFrequency (%)
Dash Punctuation 1000
69.5%
Lowercase Letter 299
 
20.8%
Space Separator 66
 
4.6%
Decimal Number 46
 
3.2%
Uppercase Letter 24
 
1.7%
Other Punctuation 4
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 48
16.1%
t 40
13.4%
a 33
11.0%
s 24
8.0%
n 23
7.7%
i 21
7.0%
v 20
6.7%
g 18
 
6.0%
r 16
 
5.4%
o 14
 
4.7%
Other values (10) 42
14.0%
Uppercase Letter
ValueCountFrequency (%)
I 14
58.3%
S 3
 
12.5%
R 2
 
8.3%
M 1
 
4.2%
T 1
 
4.2%
H 1
 
4.2%
N 1
 
4.2%
C 1
 
4.2%
Decimal Number
ValueCountFrequency (%)
8 16
34.8%
6 16
34.8%
3 4
 
8.7%
2 4
 
8.7%
1 3
 
6.5%
4 2
 
4.3%
7 1
 
2.2%
Other Punctuation
ValueCountFrequency (%)
& 3
75.0%
. 1
 
25.0%
Dash Punctuation
ValueCountFrequency (%)
- 1000
100.0%
Space Separator
ValueCountFrequency (%)
66
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1116
77.6%
Latin 323
 
22.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 48
14.9%
t 40
12.4%
a 33
10.2%
s 24
 
7.4%
n 23
 
7.1%
i 21
 
6.5%
v 20
 
6.2%
g 18
 
5.6%
r 16
 
5.0%
I 14
 
4.3%
Other values (18) 66
20.4%
Common
ValueCountFrequency (%)
- 1000
89.6%
66
 
5.9%
8 16
 
1.4%
6 16
 
1.4%
3 4
 
0.4%
2 4
 
0.4%
& 3
 
0.3%
1 3
 
0.3%
4 2
 
0.2%
7 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1439
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 1000
69.5%
66
 
4.6%
e 48
 
3.3%
t 40
 
2.8%
a 33
 
2.3%
s 24
 
1.7%
n 23
 
1.6%
i 21
 
1.5%
v 20
 
1.4%
g 18
 
1.3%
Other values (29) 146
 
10.1%

action_taken_other
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
-
997 
51 - Ventilate
 
1
20 - Search & rescue, other
 
1
86 - Investigate
 
1

Length

Max length27
Median length1
Mean length1.054
Min length1

Characters and Unicode

Total characters1054
Distinct characters27
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.3%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 997
99.7%
51 - Ventilate 1
 
0.1%
20 - Search & rescue, other 1
 
0.1%
86 - Investigate 1
 
0.1%

Length

2023-05-09T22:12:03.028640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T22:12:03.179137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1001
99.2%
51 1
 
0.1%
ventilate 1
 
0.1%
20 1
 
0.1%
search 1
 
0.1%
rescue 1
 
0.1%
other 1
 
0.1%
86 1
 
0.1%
investigate 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
- 1000
94.9%
9
 
0.9%
e 8
 
0.8%
t 5
 
0.5%
a 3
 
0.3%
r 3
 
0.3%
h 2
 
0.2%
c 2
 
0.2%
n 2
 
0.2%
i 2
 
0.2%
Other values (17) 18
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Dash Punctuation 1000
94.9%
Lowercase Letter 34
 
3.2%
Space Separator 9
 
0.9%
Decimal Number 6
 
0.6%
Uppercase Letter 3
 
0.3%
Other Punctuation 2
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8
23.5%
t 5
14.7%
a 3
 
8.8%
r 3
 
8.8%
h 2
 
5.9%
c 2
 
5.9%
n 2
 
5.9%
i 2
 
5.9%
s 2
 
5.9%
o 1
 
2.9%
Other values (4) 4
11.8%
Decimal Number
ValueCountFrequency (%)
6 1
16.7%
8 1
16.7%
5 1
16.7%
0 1
16.7%
2 1
16.7%
1 1
16.7%
Uppercase Letter
ValueCountFrequency (%)
I 1
33.3%
S 1
33.3%
V 1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 1
50.0%
& 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 1000
100.0%
Space Separator
ValueCountFrequency (%)
9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1017
96.5%
Latin 37
 
3.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8
21.6%
t 5
13.5%
a 3
 
8.1%
r 3
 
8.1%
h 2
 
5.4%
c 2
 
5.4%
n 2
 
5.4%
i 2
 
5.4%
s 2
 
5.4%
o 1
 
2.7%
Other values (7) 7
18.9%
Common
ValueCountFrequency (%)
- 1000
98.3%
9
 
0.9%
, 1
 
0.1%
6 1
 
0.1%
8 1
 
0.1%
& 1
 
0.1%
5 1
 
0.1%
0 1
 
0.1%
2 1
 
0.1%
1 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1054
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 1000
94.9%
9
 
0.9%
e 8
 
0.8%
t 5
 
0.5%
a 3
 
0.3%
r 3
 
0.3%
h 2
 
0.2%
c 2
 
0.2%
n 2
 
0.2%
i 2
 
0.2%
Other values (17) 18
 
1.7%

detector_alerted_occupants
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
-
933 
U - Unknown
 
50
1 - Detector alerted occupants
 
12
2 - Detector did not alert occupants
 
5

Length

Max length36
Median length1
Mean length2.023
Min length1

Characters and Unicode

Total characters2023
Distinct characters21
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 933
93.3%
U - Unknown 50
 
5.0%
1 - Detector alerted occupants 12
 
1.2%
2 - Detector did not alert occupants 5
 
0.5%

Length

2023-05-09T22:12:03.326406image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T22:12:03.543833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1000
84.9%
u 50
 
4.2%
unknown 50
 
4.2%
detector 17
 
1.4%
occupants 17
 
1.4%
1 12
 
1.0%
alerted 12
 
1.0%
2 5
 
0.4%
did 5
 
0.4%
not 5
 
0.4%

Most occurring characters

ValueCountFrequency (%)
- 1000
49.4%
178
 
8.8%
n 172
 
8.5%
U 100
 
4.9%
o 89
 
4.4%
t 73
 
3.6%
e 63
 
3.1%
c 51
 
2.5%
w 50
 
2.5%
k 50
 
2.5%
Other values (11) 197
 
9.7%

Most occurring categories

ValueCountFrequency (%)
Dash Punctuation 1000
49.4%
Lowercase Letter 711
35.1%
Space Separator 178
 
8.8%
Uppercase Letter 117
 
5.8%
Decimal Number 17
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 172
24.2%
o 89
12.5%
t 73
10.3%
e 63
 
8.9%
c 51
 
7.2%
w 50
 
7.0%
k 50
 
7.0%
r 34
 
4.8%
a 34
 
4.8%
d 22
 
3.1%
Other values (5) 73
10.3%
Uppercase Letter
ValueCountFrequency (%)
U 100
85.5%
D 17
 
14.5%
Decimal Number
ValueCountFrequency (%)
1 12
70.6%
2 5
29.4%
Dash Punctuation
ValueCountFrequency (%)
- 1000
100.0%
Space Separator
ValueCountFrequency (%)
178
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1195
59.1%
Latin 828
40.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 172
20.8%
U 100
12.1%
o 89
10.7%
t 73
8.8%
e 63
 
7.6%
c 51
 
6.2%
w 50
 
6.0%
k 50
 
6.0%
r 34
 
4.1%
a 34
 
4.1%
Other values (7) 112
13.5%
Common
ValueCountFrequency (%)
- 1000
83.7%
178
 
14.9%
1 12
 
1.0%
2 5
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2023
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 1000
49.4%
178
 
8.8%
n 172
 
8.5%
U 100
 
4.9%
o 89
 
4.4%
t 73
 
3.6%
e 63
 
3.1%
c 51
 
2.5%
w 50
 
2.5%
k 50
 
2.5%
Other values (11) 197
 
9.7%

property_use
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct62
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
962 - Residential street, road or residential dr
200 
960 - Street, other
163 
429 - Multifamily dwellings
157 
419 - 1 or 2 family dwelling
104 
963 - Street or road in commercial area
82 
Other values (57)
294 

Length

Max length48
Median length46
Mean length31.325
Min length1

Characters and Unicode

Total characters31325
Distinct characters63
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)2.5%

Sample

1st row962 - Residential street, road or residential dr
2nd row960 - Street, other
3rd row429 - Multifamily dwellings
4th row400 - Residential, other
5th row960 - Street, other

Common Values

ValueCountFrequency (%)
962 - Residential street, road or residential dr 200
20.0%
960 - Street, other 163
16.3%
429 - Multifamily dwellings 157
15.7%
419 - 1 or 2 family dwelling 104
10.4%
963 - Street or road in commercial area 82
8.2%
000 - Property Use, other 54
 
5.4%
599 - Business office 44
 
4.4%
400 - Residential, other 23
 
2.3%
150 - Public or government, other 20
 
2.0%
331 - Hospital - medical or psychiatric 14
 
1.4%
Other values (52) 139
13.9%

Length

2023-05-09T22:12:03.803150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1021
17.8%
street 453
 
7.9%
or 444
 
7.7%
residential 428
 
7.5%
other 284
 
5.0%
road 282
 
4.9%
962 200
 
3.5%
dr 200
 
3.5%
960 163
 
2.8%
429 157
 
2.7%
Other values (187) 2101
36.6%

Most occurring characters

ValueCountFrequency (%)
4733
15.1%
e 3080
 
9.8%
r 2393
 
7.6%
i 2068
 
6.6%
t 2053
 
6.6%
l 1622
 
5.2%
a 1460
 
4.7%
o 1423
 
4.5%
d 1276
 
4.1%
s 1146
 
3.7%
Other values (53) 10071
32.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 20753
66.3%
Space Separator 4733
 
15.1%
Decimal Number 3181
 
10.2%
Dash Punctuation 1019
 
3.3%
Uppercase Letter 996
 
3.2%
Other Punctuation 627
 
2.0%
Close Punctuation 8
 
< 0.1%
Open Punctuation 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3080
14.8%
r 2393
11.5%
i 2068
10.0%
t 2053
9.9%
l 1622
7.8%
a 1460
7.0%
o 1423
6.9%
d 1276
 
6.1%
s 1146
 
5.5%
n 1042
 
5.0%
Other values (14) 3190
15.4%
Uppercase Letter
ValueCountFrequency (%)
S 257
25.8%
R 236
23.7%
M 167
16.8%
U 86
 
8.6%
P 81
 
8.1%
B 58
 
5.8%
H 38
 
3.8%
N 12
 
1.2%
D 12
 
1.2%
V 9
 
0.9%
Other values (9) 40
 
4.0%
Decimal Number
ValueCountFrequency (%)
9 852
26.8%
2 481
15.1%
6 472
14.8%
0 447
14.1%
4 333
 
10.5%
1 320
 
10.1%
3 147
 
4.6%
5 90
 
2.8%
8 32
 
1.0%
7 7
 
0.2%
Other Punctuation
ValueCountFrequency (%)
, 543
86.6%
/ 41
 
6.5%
. 33
 
5.3%
& 7
 
1.1%
; 2
 
0.3%
: 1
 
0.2%
Space Separator
ValueCountFrequency (%)
4733
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1019
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 21749
69.4%
Common 9576
30.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3080
14.2%
r 2393
11.0%
i 2068
9.5%
t 2053
9.4%
l 1622
 
7.5%
a 1460
 
6.7%
o 1423
 
6.5%
d 1276
 
5.9%
s 1146
 
5.3%
n 1042
 
4.8%
Other values (33) 4186
19.2%
Common
ValueCountFrequency (%)
4733
49.4%
- 1019
 
10.6%
9 852
 
8.9%
, 543
 
5.7%
2 481
 
5.0%
6 472
 
4.9%
0 447
 
4.7%
4 333
 
3.5%
1 320
 
3.3%
3 147
 
1.5%
Other values (10) 229
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31325
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4733
15.1%
e 3080
 
9.8%
r 2393
 
7.6%
i 2068
 
6.6%
t 2053
 
6.6%
l 1622
 
5.2%
a 1460
 
4.7%
o 1423
 
4.5%
d 1276
 
4.1%
s 1146
 
3.7%
Other values (53) 10071
32.2%

supervisor_district
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)3.7%
Missing781
Missing (%)78.1%
Infinite0
Infinite (%)0.0%
Mean4.2009132
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-05-09T22:12:03.974205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q35
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.1214931
Coefficient of variation (CV)0.5050076
Kurtosis0.11042654
Mean4.2009132
Median Absolute Deviation (MAD)2
Skewness0.623918
Sum920
Variance4.5007331
MonotonicityNot monotonic
2023-05-09T22:12:04.111091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5 82
 
8.2%
3 45
 
4.5%
2 30
 
3.0%
1 20
 
2.0%
9 19
 
1.9%
4 11
 
1.1%
7 8
 
0.8%
6 4
 
0.4%
(Missing) 781
78.1%
ValueCountFrequency (%)
1 20
 
2.0%
2 30
 
3.0%
3 45
4.5%
4 11
 
1.1%
5 82
8.2%
6 4
 
0.4%
7 8
 
0.8%
9 19
 
1.9%
ValueCountFrequency (%)
9 19
 
1.9%
7 8
 
0.8%
6 4
 
0.4%
5 82
8.2%
4 11
 
1.1%
3 45
4.5%
2 30
 
3.0%
1 20
 
2.0%
Distinct41
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Mission
89 
Financial District/South Beach
81 
Tenderloin
66 
Bayview Hunters Point
 
63
South of Market
 
56
Other values (36)
645 

Length

Max length30
Median length18
Mean length14.599
Min length6

Characters and Unicode

Total characters14599
Distinct characters46
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowBernal Heights
2nd rowPotrero Hill
3rd rowSouth of Market
4th rowHayes Valley
5th rowOuter Richmond

Common Values

ValueCountFrequency (%)
Mission 89
 
8.9%
Financial District/South Beach 81
 
8.1%
Tenderloin 66
 
6.6%
Bayview Hunters Point 63
 
6.3%
South of Market 56
 
5.6%
Sunset/Parkside 48
 
4.8%
Pacific Heights 41
 
4.1%
Western Addition 36
 
3.6%
Potrero Hill 36
 
3.6%
Nob Hill 32
 
3.2%
Other values (31) 452
45.2%

Length

2023-05-09T22:12:04.288624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mission 107
 
5.5%
beach 99
 
5.1%
heights 81
 
4.2%
district/south 81
 
4.2%
financial 81
 
4.2%
of 81
 
4.2%
hill 80
 
4.1%
market 79
 
4.1%
tenderloin 66
 
3.4%
bayview 63
 
3.3%
Other values (46) 1110
57.6%

Most occurring characters

ValueCountFrequency (%)
i 1433
 
9.8%
e 1296
 
8.9%
n 1081
 
7.4%
t 973
 
6.7%
928
 
6.4%
a 920
 
6.3%
s 842
 
5.8%
o 842
 
5.8%
r 765
 
5.2%
l 522
 
3.6%
Other values (36) 4997
34.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11344
77.7%
Uppercase Letter 2103
 
14.4%
Space Separator 928
 
6.4%
Other Punctuation 224
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 1433
12.6%
e 1296
11.4%
n 1081
9.5%
t 973
8.6%
a 920
8.1%
s 842
 
7.4%
o 842
 
7.4%
r 765
 
6.7%
l 522
 
4.6%
c 482
 
4.2%
Other values (13) 2188
19.3%
Uppercase Letter
ValueCountFrequency (%)
H 277
13.2%
P 263
12.5%
M 260
12.4%
S 221
10.5%
B 194
9.2%
T 100
 
4.8%
F 95
 
4.5%
D 81
 
3.9%
V 79
 
3.8%
O 68
 
3.2%
Other values (11) 465
22.1%
Space Separator
ValueCountFrequency (%)
928
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 224
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13447
92.1%
Common 1152
 
7.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 1433
 
10.7%
e 1296
 
9.6%
n 1081
 
8.0%
t 973
 
7.2%
a 920
 
6.8%
s 842
 
6.3%
o 842
 
6.3%
r 765
 
5.7%
l 522
 
3.9%
c 482
 
3.6%
Other values (34) 4291
31.9%
Common
ValueCountFrequency (%)
928
80.6%
/ 224
 
19.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14599
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 1433
 
9.8%
e 1296
 
8.9%
n 1081
 
7.4%
t 973
 
6.7%
928
 
6.4%
a 920
 
6.3%
s 842
 
5.8%
o 842
 
5.8%
r 765
 
5.2%
l 522
 
3.6%
Other values (36) 4997
34.2%

point
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct856
Distinct (%)85.6%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
{'type': 'Point', 'coordinates': [-122.400474, 37.785029]}
 
5
{'type': 'Point', 'coordinates': [-122.408953, 37.783288]}
 
5
{'type': 'Point', 'coordinates': [-122.459024, 37.713172]}
 
5
{'type': 'Point', 'coordinates': [-122.41936, 37.761836]}
 
5
{'type': 'Point', 'coordinates': [-122.409237, 37.76575]}
 
4
Other values (851)
976 

Length

Max length75
Median length58
Mean length59.263
Min length55

Characters and Unicode

Total characters59263
Distinct characters33
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique747 ?
Unique (%)74.7%

Sample

1st row{'type': 'Point', 'coordinates': [-122.41837339, 37.74208979]}
2nd row{'type': 'Point', 'coordinates': [-122.39489, 37.756291]}
3rd row{'type': 'Point', 'coordinates': [-122.407468, 37.78008]}
4th row{'type': 'Point', 'coordinates': [-122.42684908, 37.77612642]}
5th row{'type': 'Point', 'coordinates': [-122.4863941, 37.77428492]}

Common Values

ValueCountFrequency (%)
{'type': 'Point', 'coordinates': [-122.400474, 37.785029]} 5
 
0.5%
{'type': 'Point', 'coordinates': [-122.408953, 37.783288]} 5
 
0.5%
{'type': 'Point', 'coordinates': [-122.459024, 37.713172]} 5
 
0.5%
{'type': 'Point', 'coordinates': [-122.41936, 37.761836]} 5
 
0.5%
{'type': 'Point', 'coordinates': [-122.409237, 37.76575]} 4
 
0.4%
{'type': 'Point', 'coordinates': [-122.418928, 37.774844]} 4
 
0.4%
{'type': 'Point', 'coordinates': [-122.39835485566738, 37.72309648958183]} 3
 
0.3%
{'type': 'Point', 'coordinates': [-122.481573, 37.718991]} 3
 
0.3%
{'type': 'Point', 'coordinates': [-122.418749, 37.755437]} 3
 
0.3%
{'type': 'Point', 'coordinates': [-122.404617, 37.793565]} 3
 
0.3%
Other values (846) 960
96.0%

Length

2023-05-09T22:12:04.703336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
type 1000
20.0%
coordinates 1000
20.0%
point 1000
20.0%
122.400474 5
 
0.1%
37.785029 5
 
0.1%
122.408953 5
 
0.1%
37.783288 5
 
0.1%
122.459024 5
 
0.1%
37.713172 5
 
0.1%
122.41936 5
 
0.1%
Other values (1704) 1965
39.3%

Most occurring characters

ValueCountFrequency (%)
' 6000
 
10.1%
4000
 
6.7%
2 3097
 
5.2%
7 3057
 
5.2%
t 3000
 
5.1%
o 3000
 
5.1%
3 2261
 
3.8%
1 2144
 
3.6%
n 2000
 
3.4%
. 2000
 
3.4%
Other values (23) 28704
48.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 19000
32.1%
Decimal Number 18263
30.8%
Other Punctuation 12000
20.2%
Space Separator 4000
 
6.7%
Close Punctuation 2000
 
3.4%
Open Punctuation 2000
 
3.4%
Dash Punctuation 1000
 
1.7%
Uppercase Letter 1000
 
1.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 3000
15.8%
o 3000
15.8%
n 2000
10.5%
i 2000
10.5%
e 2000
10.5%
s 1000
 
5.3%
d 1000
 
5.3%
r 1000
 
5.3%
c 1000
 
5.3%
p 1000
 
5.3%
Other values (2) 2000
10.5%
Decimal Number
ValueCountFrequency (%)
2 3097
17.0%
7 3057
16.7%
3 2261
12.4%
1 2144
11.7%
4 1954
10.7%
8 1333
7.3%
9 1199
 
6.6%
5 1117
 
6.1%
6 1114
 
6.1%
0 987
 
5.4%
Other Punctuation
ValueCountFrequency (%)
' 6000
50.0%
. 2000
 
16.7%
, 2000
 
16.7%
: 2000
 
16.7%
Close Punctuation
ValueCountFrequency (%)
] 1000
50.0%
} 1000
50.0%
Open Punctuation
ValueCountFrequency (%)
{ 1000
50.0%
[ 1000
50.0%
Space Separator
ValueCountFrequency (%)
4000
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1000
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 39263
66.3%
Latin 20000
33.7%

Most frequent character per script

Common
ValueCountFrequency (%)
' 6000
15.3%
4000
 
10.2%
2 3097
 
7.9%
7 3057
 
7.8%
3 2261
 
5.8%
1 2144
 
5.5%
. 2000
 
5.1%
, 2000
 
5.1%
: 2000
 
5.1%
4 1954
 
5.0%
Other values (10) 10750
27.4%
Latin
ValueCountFrequency (%)
t 3000
15.0%
o 3000
15.0%
n 2000
10.0%
i 2000
10.0%
e 2000
10.0%
s 1000
 
5.0%
d 1000
 
5.0%
r 1000
 
5.0%
c 1000
 
5.0%
P 1000
 
5.0%
Other values (3) 3000
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 59263
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 6000
 
10.1%
4000
 
6.7%
2 3097
 
5.2%
7 3057
 
5.2%
t 3000
 
5.1%
o 3000
 
5.1%
3 2261
 
3.8%
1 2144
 
3.6%
n 2000
 
3.4%
. 2000
 
3.4%
Other values (23) 28704
48.4%

estimated_contents_loss
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct4
Distinct (%)0.4%
Missing61
Missing (%)6.1%
Memory size7.9 KiB
0.0
936 
1.0
 
1
100.0
 
1
300.0
 
1

Length

Max length5
Median length3
Mean length3.0042599
Min length3

Characters and Unicode

Total characters2821
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.3%

Sample

1st row1.0
2nd row100.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 936
93.6%
1.0 1
 
0.1%
100.0 1
 
0.1%
300.0 1
 
0.1%
(Missing) 61
 
6.1%

Length

2023-05-09T22:12:04.881963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T22:12:05.104517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 936
99.7%
1.0 1
 
0.1%
100.0 1
 
0.1%
300.0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1879
66.6%
. 939
33.3%
1 2
 
0.1%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1882
66.7%
Other Punctuation 939
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1879
99.8%
1 2
 
0.1%
3 1
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 939
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2821
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1879
66.6%
. 939
33.3%
1 2
 
0.1%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2821
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1879
66.6%
. 939
33.3%
1 2
 
0.1%
3 1
 
< 0.1%

area_of_fire_origin
Categorical

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)32.0%
Missing950
Missing (%)95.0%
Memory size7.9 KiB
-
11 
24 - Cooking area, kitchen
80 - Vehicle area, other
90 - Outside area, other
21 - Bedroom-<5 persons; inc. jail or pris
Other values (11)
18 

Length

Max length42
Median length41
Mean length25.18
Min length1

Characters and Unicode

Total characters1259
Distinct characters50
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)14.0%

Sample

1st row24 - Cooking area, kitchen
2nd row90 - Outside area, other
3rd row21 - Bedroom-<5 persons; inc. jail or pris
4th row90 - Outside area, other
5th row21 - Bedroom-<5 persons; inc. jail or pris

Common Values

ValueCountFrequency (%)
- 11
 
1.1%
24 - Cooking area, kitchen 9
 
0.9%
80 - Vehicle area, other 4
 
0.4%
90 - Outside area, other 4
 
0.4%
21 - Bedroom-<5 persons; inc. jail or pris 4
 
0.4%
83 - Engine area, running gear, wheel area 4
 
0.4%
81 - Operator/passenger area/transport equ 3
 
0.3%
14 - Common room, den, family/living room 2
 
0.2%
92 - Highway, parking lot, street: on or n 2
 
0.2%
UU - Undetermined 1
 
0.1%
Other values (6) 6
 
0.6%
(Missing) 950
95.0%

Length

2023-05-09T22:12:05.266170image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
51
20.8%
area 26
 
10.6%
cooking 9
 
3.7%
kitchen 9
 
3.7%
24 9
 
3.7%
other 9
 
3.7%
or 7
 
2.9%
room 5
 
2.0%
outside 5
 
2.0%
inc 5
 
2.0%
Other values (51) 110
44.9%

Most occurring characters

ValueCountFrequency (%)
195
15.5%
e 115
 
9.1%
r 106
 
8.4%
a 92
 
7.3%
o 79
 
6.3%
n 69
 
5.5%
i 64
 
5.1%
- 55
 
4.4%
t 43
 
3.4%
, 38
 
3.0%
Other values (40) 403
32.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 825
65.5%
Space Separator 195
 
15.5%
Decimal Number 80
 
6.4%
Other Punctuation 59
 
4.7%
Dash Punctuation 55
 
4.4%
Uppercase Letter 41
 
3.3%
Math Symbol 4
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 115
13.9%
r 106
12.8%
a 92
11.2%
o 79
9.6%
n 69
8.4%
i 64
 
7.8%
t 43
 
5.2%
s 31
 
3.8%
h 30
 
3.6%
g 30
 
3.6%
Other values (14) 166
20.1%
Decimal Number
ValueCountFrequency (%)
2 17
21.2%
0 12
15.0%
4 12
15.0%
8 11
13.8%
1 10
12.5%
9 7
8.8%
5 5
 
6.2%
3 4
 
5.0%
7 1
 
1.2%
6 1
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
C 12
29.3%
O 9
22.0%
E 5
12.2%
B 5
12.2%
V 4
 
9.8%
U 3
 
7.3%
H 2
 
4.9%
W 1
 
2.4%
Other Punctuation
ValueCountFrequency (%)
, 38
64.4%
/ 8
 
13.6%
. 5
 
8.5%
; 5
 
8.5%
: 3
 
5.1%
Space Separator
ValueCountFrequency (%)
195
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 55
100.0%
Math Symbol
ValueCountFrequency (%)
< 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 866
68.8%
Common 393
31.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 115
13.3%
r 106
12.2%
a 92
10.6%
o 79
 
9.1%
n 69
 
8.0%
i 64
 
7.4%
t 43
 
5.0%
s 31
 
3.6%
h 30
 
3.5%
g 30
 
3.5%
Other values (22) 207
23.9%
Common
ValueCountFrequency (%)
195
49.6%
- 55
 
14.0%
, 38
 
9.7%
2 17
 
4.3%
0 12
 
3.1%
4 12
 
3.1%
8 11
 
2.8%
1 10
 
2.5%
/ 8
 
2.0%
9 7
 
1.8%
Other values (8) 28
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1259
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
195
15.5%
e 115
 
9.1%
r 106
 
8.4%
a 92
 
7.3%
o 79
 
6.3%
n 69
 
5.5%
i 64
 
5.1%
- 55
 
4.4%
t 43
 
3.4%
, 38
 
3.0%
Other values (40) 403
32.0%

ignition_cause
Categorical

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)12.0%
Missing950
Missing (%)95.0%
Memory size7.9 KiB
2 - Unintentional
19 
-
11 
U - Cause undetermined after investigation
10 
1 - Intentional
3 - Failure of equipment or heat source

Length

Max length42
Median length39
Mean length20.08
Min length1

Characters and Unicode

Total characters1004
Distinct characters29
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2 - Unintentional
2nd rowU - Cause undetermined after investigation
3rd row3 - Failure of equipment or heat source
4th row2 - Unintentional
5th row2 - Unintentional

Common Values

ValueCountFrequency (%)
2 - Unintentional 19
 
1.9%
- 11
 
1.1%
U - Cause undetermined after investigation 10
 
1.0%
1 - Intentional 5
 
0.5%
3 - Failure of equipment or heat source 3
 
0.3%
5 - Cause under investigation 2
 
0.2%
(Missing) 950
95.0%

Length

2023-05-09T22:12:05.442728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T22:12:05.624748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
50
28.2%
2 19
 
10.7%
unintentional 19
 
10.7%
cause 12
 
6.8%
investigation 12
 
6.8%
u 10
 
5.6%
undetermined 10
 
5.6%
after 10
 
5.6%
intentional 5
 
2.8%
1 5
 
2.8%
Other values (9) 25
14.1%

Most occurring characters

ValueCountFrequency (%)
n 140
13.9%
127
12.6%
e 105
10.5%
t 98
9.8%
i 95
9.5%
a 64
 
6.4%
- 50
 
5.0%
o 45
 
4.5%
u 33
 
3.3%
r 31
 
3.1%
Other values (19) 216
21.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 749
74.6%
Space Separator 127
 
12.6%
Dash Punctuation 50
 
5.0%
Uppercase Letter 49
 
4.9%
Decimal Number 29
 
2.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 140
18.7%
e 105
14.0%
t 98
13.1%
i 95
12.7%
a 64
8.5%
o 45
 
6.0%
u 33
 
4.4%
r 31
 
4.1%
l 27
 
3.6%
s 27
 
3.6%
Other values (9) 84
11.2%
Uppercase Letter
ValueCountFrequency (%)
U 29
59.2%
C 12
24.5%
I 5
 
10.2%
F 3
 
6.1%
Decimal Number
ValueCountFrequency (%)
2 19
65.5%
1 5
 
17.2%
3 3
 
10.3%
5 2
 
6.9%
Space Separator
ValueCountFrequency (%)
127
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 50
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 798
79.5%
Common 206
 
20.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 140
17.5%
e 105
13.2%
t 98
12.3%
i 95
11.9%
a 64
8.0%
o 45
 
5.6%
u 33
 
4.1%
r 31
 
3.9%
U 29
 
3.6%
l 27
 
3.4%
Other values (13) 131
16.4%
Common
ValueCountFrequency (%)
127
61.7%
- 50
 
24.3%
2 19
 
9.2%
1 5
 
2.4%
3 3
 
1.5%
5 2
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1004
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 140
13.9%
127
12.6%
e 105
10.5%
t 98
9.8%
i 95
9.5%
a 64
 
6.4%
- 50
 
5.0%
o 45
 
4.5%
u 33
 
3.3%
r 31
 
3.1%
Other values (19) 216
21.5%

ignition_factor_primary
Categorical

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)22.0%
Missing950
Missing (%)95.0%
Memory size7.9 KiB
-
27 
53 - Equipment unattended
12 - Heat source too close to combustibles
UU - Undetermined
11 - Abandoned or discarded materials or p
Other values (6)

Length

Max length42
Median length1
Mean length15.68
Min length1

Characters and Unicode

Total characters784
Distinct characters35
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)8.0%

Sample

1st row12 - Heat source too close to combustibles
2nd rowUU - Undetermined
3rd row30 - Electrical failure, malfunction, othe
4th row11 - Abandoned or discarded materials or p
5th row53 - Equipment unattended

Common Values

ValueCountFrequency (%)
- 27
 
2.7%
53 - Equipment unattended 5
 
0.5%
12 - Heat source too close to combustibles 4
 
0.4%
UU - Undetermined 3
 
0.3%
11 - Abandoned or discarded materials or p 3
 
0.3%
20 - Mechanical failure, malfunction, othe 2
 
0.2%
55 - Failure to clean 2
 
0.2%
30 - Electrical failure, malfunction, othe 1
 
0.1%
10 - Misuse of material or product, other 1
 
0.1%
52 - Accidentally turned on, not turned of 1
 
0.1%
(Missing) 950
95.0%

Length

2023-05-09T22:12:05.804532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
50
30.7%
or 8
 
4.9%
to 6
 
3.7%
equipment 5
 
3.1%
unattended 5
 
3.1%
53 5
 
3.1%
failure 5
 
3.1%
12 4
 
2.5%
heat 4
 
2.5%
source 4
 
2.5%
Other values (34) 67
41.1%

Most occurring characters

ValueCountFrequency (%)
113
14.4%
e 70
 
8.9%
t 53
 
6.8%
- 50
 
6.4%
o 49
 
6.2%
n 42
 
5.4%
a 42
 
5.4%
d 37
 
4.7%
i 35
 
4.5%
l 33
 
4.2%
Other values (25) 260
33.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 544
69.4%
Space Separator 113
 
14.4%
Dash Punctuation 50
 
6.4%
Decimal Number 40
 
5.1%
Uppercase Letter 29
 
3.7%
Other Punctuation 8
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 70
12.9%
t 53
9.7%
o 49
9.0%
n 42
 
7.7%
a 42
 
7.7%
d 37
 
6.8%
i 35
 
6.4%
l 33
 
6.1%
r 32
 
5.9%
u 31
 
5.7%
Other values (10) 120
22.1%
Decimal Number
ValueCountFrequency (%)
1 12
30.0%
5 10
25.0%
2 7
17.5%
3 6
15.0%
0 4
 
10.0%
4 1
 
2.5%
Uppercase Letter
ValueCountFrequency (%)
U 9
31.0%
E 6
20.7%
H 4
13.8%
A 4
13.8%
M 3
 
10.3%
F 3
 
10.3%
Space Separator
ValueCountFrequency (%)
113
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 50
100.0%
Other Punctuation
ValueCountFrequency (%)
, 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 573
73.1%
Common 211
 
26.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 70
12.2%
t 53
 
9.2%
o 49
 
8.6%
n 42
 
7.3%
a 42
 
7.3%
d 37
 
6.5%
i 35
 
6.1%
l 33
 
5.8%
r 32
 
5.6%
u 31
 
5.4%
Other values (16) 149
26.0%
Common
ValueCountFrequency (%)
113
53.6%
- 50
23.7%
1 12
 
5.7%
5 10
 
4.7%
, 8
 
3.8%
2 7
 
3.3%
3 6
 
2.8%
0 4
 
1.9%
4 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 784
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
113
14.4%
e 70
 
8.9%
t 53
 
6.8%
- 50
 
6.4%
o 49
 
6.2%
n 42
 
5.4%
a 42
 
5.4%
d 37
 
4.7%
i 35
 
4.5%
l 33
 
4.2%
Other values (25) 260
33.2%

ignition_factor_secondary
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)2.0%
Missing950
Missing (%)95.0%
Memory size7.9 KiB
-
50 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 50
 
5.0%
(Missing) 950
95.0%

Length

2023-05-09T22:12:06.065125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T22:12:06.305651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
50
100.0%

Most occurring characters

ValueCountFrequency (%)
- 50
100.0%

Most occurring categories

ValueCountFrequency (%)
Dash Punctuation 50
100.0%

Most frequent character per category

Dash Punctuation
ValueCountFrequency (%)
- 50
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 50
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 50
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 50
100.0%

heat_source
Categorical

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)32.0%
Missing950
Missing (%)95.0%
Memory size7.9 KiB
UU - Undetermined
11 
-
11 
12 - Radiated/conducted heat operating equ
10 - Heat from powered equipment, other
13 - Arcing
Other values (11)
14 

Length

Max length42
Median length37
Mean length21.68
Min length1

Characters and Unicode

Total characters1084
Distinct characters46
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)16.0%

Sample

1st row81 - Heat; direct flame or convection
2nd rowUU - Undetermined
3rd row13 - Arcing
4th rowUU - Undetermined
5th row12 - Radiated/conducted heat operating equ

Common Values

ValueCountFrequency (%)
UU - Undetermined 11
 
1.1%
- 11
 
1.1%
12 - Radiated/conducted heat operating equ 7
 
0.7%
10 - Heat from powered equipment, other 5
 
0.5%
13 - Arcing 2
 
0.2%
43 - Hot ember or ash 2
 
0.2%
00 - Heat source: other 2
 
0.2%
61 - Cigarette 2
 
0.2%
60 - Heat; other open flame/smoking materi 1
 
0.1%
81 - Heat; direct flame or convection 1
 
0.1%
Other values (6) 6
 
0.6%
(Missing) 950
95.0%

Length

2023-05-09T22:12:06.463694image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
50
25.0%
heat 17
 
8.5%
undetermined 12
 
6.0%
uu 11
 
5.5%
operating 8
 
4.0%
from 8
 
4.0%
other 8
 
4.0%
12 7
 
3.5%
radiated/conducted 7
 
3.5%
equ 7
 
3.5%
Other values (39) 65
32.5%

Most occurring characters

ValueCountFrequency (%)
150
13.8%
e 140
12.9%
t 80
 
7.4%
r 62
 
5.7%
d 60
 
5.5%
n 57
 
5.3%
a 54
 
5.0%
- 50
 
4.6%
i 50
 
4.6%
o 50
 
4.6%
Other values (36) 331
30.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 748
69.0%
Space Separator 150
 
13.8%
Uppercase Letter 61
 
5.6%
Decimal Number 56
 
5.2%
Dash Punctuation 50
 
4.6%
Other Punctuation 19
 
1.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 140
18.7%
t 80
10.7%
r 62
8.3%
d 60
8.0%
n 57
7.6%
a 54
 
7.2%
i 50
 
6.7%
o 50
 
6.7%
m 36
 
4.8%
c 26
 
3.5%
Other values (13) 133
17.8%
Uppercase Letter
ValueCountFrequency (%)
U 33
54.1%
H 12
 
19.7%
R 7
 
11.5%
C 3
 
4.9%
A 2
 
3.3%
S 1
 
1.6%
I 1
 
1.6%
M 1
 
1.6%
B 1
 
1.6%
Decimal Number
ValueCountFrequency (%)
1 19
33.9%
0 10
17.9%
6 8
14.3%
2 7
 
12.5%
3 5
 
8.9%
4 3
 
5.4%
8 2
 
3.6%
5 2
 
3.6%
Other Punctuation
ValueCountFrequency (%)
/ 10
52.6%
, 5
26.3%
; 2
 
10.5%
: 2
 
10.5%
Space Separator
ValueCountFrequency (%)
150
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 50
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 809
74.6%
Common 275
 
25.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 140
17.3%
t 80
9.9%
r 62
 
7.7%
d 60
 
7.4%
n 57
 
7.0%
a 54
 
6.7%
i 50
 
6.2%
o 50
 
6.2%
m 36
 
4.4%
U 33
 
4.1%
Other values (22) 187
23.1%
Common
ValueCountFrequency (%)
150
54.5%
- 50
 
18.2%
1 19
 
6.9%
/ 10
 
3.6%
0 10
 
3.6%
6 8
 
2.9%
2 7
 
2.5%
, 5
 
1.8%
3 5
 
1.8%
4 3
 
1.1%
Other values (4) 8
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1084
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
150
13.8%
e 140
12.9%
t 80
 
7.4%
r 62
 
5.7%
d 60
 
5.5%
n 57
 
5.3%
a 54
 
5.0%
- 50
 
4.6%
i 50
 
4.6%
o 50
 
4.6%
Other values (36) 331
30.5%

item_first_ignited
Categorical

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)36.0%
Missing950
Missing (%)95.0%
Memory size7.9 KiB
-
11 
76 - Cooking materials, inc. Edible materi
UU - Undetermined
81 - Electrical wire, cable insulation
96 - Rubbish, trash, or waste
Other values (13)
16 

Length

Max length42
Median length41
Mean length26.3
Min length1

Characters and Unicode

Total characters1315
Distinct characters51
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)22.0%

Sample

1st row76 - Cooking materials, inc. Edible materi
2nd row70 - Organic materials, other
3rd row81 - Electrical wire, cable insulation
4th row96 - Rubbish, trash, or waste
5th row14 - Floor covering or rug/carpet/mat

Common Values

ValueCountFrequency (%)
- 11
 
1.1%
76 - Cooking materials, inc. Edible materi 9
 
0.9%
UU - Undetermined 8
 
0.8%
81 - Electrical wire, cable insulation 3
 
0.3%
96 - Rubbish, trash, or waste 3
 
0.3%
21 - Upholstered sofa, chair, vehicle seat 3
 
0.3%
14 - Floor covering or rug/carpet/mat 2
 
0.2%
15 - Int. Wall cover exclude drapes, etc. 1
 
0.1%
62 - Flam. liq/gas-in/from engine or burne 1
 
0.1%
51 - Box, carton, bag, basket, barrel 1
 
0.1%
Other values (8) 8
 
0.8%
(Missing) 950
95.0%

Length

2023-05-09T22:12:06.951791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
51
21.5%
materials 10
 
4.2%
inc 10
 
4.2%
76 9
 
3.8%
cooking 9
 
3.8%
edible 9
 
3.8%
materi 9
 
3.8%
undetermined 8
 
3.4%
uu 8
 
3.4%
or 7
 
3.0%
Other values (70) 107
45.1%

Most occurring characters

ValueCountFrequency (%)
188
 
14.3%
e 108
 
8.2%
i 98
 
7.5%
r 80
 
6.1%
a 77
 
5.9%
t 68
 
5.2%
n 60
 
4.6%
o 59
 
4.5%
l 55
 
4.2%
- 52
 
4.0%
Other values (41) 470
35.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 891
67.8%
Space Separator 188
 
14.3%
Uppercase Letter 65
 
4.9%
Decimal Number 62
 
4.7%
Other Punctuation 56
 
4.3%
Dash Punctuation 52
 
4.0%
Open Punctuation 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 108
12.1%
i 98
11.0%
r 80
 
9.0%
a 77
 
8.6%
t 68
 
7.6%
n 60
 
6.7%
o 59
 
6.6%
l 55
 
6.2%
s 42
 
4.7%
c 42
 
4.7%
Other values (13) 202
22.7%
Uppercase Letter
ValueCountFrequency (%)
U 27
41.5%
E 12
18.5%
C 11
16.9%
R 4
 
6.2%
F 4
 
6.2%
W 1
 
1.5%
B 1
 
1.5%
M 1
 
1.5%
L 1
 
1.5%
I 1
 
1.5%
Other values (2) 2
 
3.1%
Decimal Number
ValueCountFrequency (%)
6 14
22.6%
1 13
21.0%
7 11
17.7%
2 5
 
8.1%
4 5
 
8.1%
9 4
 
6.5%
8 4
 
6.5%
5 3
 
4.8%
0 2
 
3.2%
3 1
 
1.6%
Other Punctuation
ValueCountFrequency (%)
, 37
66.1%
. 13
 
23.2%
/ 6
 
10.7%
Space Separator
ValueCountFrequency (%)
188
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 52
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 956
72.7%
Common 359
 
27.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 108
11.3%
i 98
 
10.3%
r 80
 
8.4%
a 77
 
8.1%
t 68
 
7.1%
n 60
 
6.3%
o 59
 
6.2%
l 55
 
5.8%
s 42
 
4.4%
c 42
 
4.4%
Other values (25) 267
27.9%
Common
ValueCountFrequency (%)
188
52.4%
- 52
 
14.5%
, 37
 
10.3%
6 14
 
3.9%
1 13
 
3.6%
. 13
 
3.6%
7 11
 
3.1%
/ 6
 
1.7%
2 5
 
1.4%
4 5
 
1.4%
Other values (6) 15
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1315
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
188
 
14.3%
e 108
 
8.2%
i 98
 
7.5%
r 80
 
6.1%
a 77
 
5.9%
t 68
 
5.2%
n 60
 
4.6%
o 59
 
4.5%
l 55
 
4.2%
- 52
 
4.0%
Other values (41) 470
35.7%

human_factors_associated_with_ignition
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)8.0%
Missing950
Missing (%)95.0%
Memory size7.9 KiB
-
29 
N - None
12 
3 - Unattended or unsupervised person
5 - Physically disabled
 
1

Length

Max length37
Median length1
Mean length8.88
Min length1

Characters and Unicode

Total characters444
Distinct characters24
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st row3 - Unattended or unsupervised person
2nd row3 - Unattended or unsupervised person
3rd rowN - None
4th rowN - None
5th rowN - None

Common Values

ValueCountFrequency (%)
- 29
 
2.9%
N - None 12
 
1.2%
3 - Unattended or unsupervised person 8
 
0.8%
5 - Physically disabled 1
 
0.1%
(Missing) 950
95.0%

Length

2023-05-09T22:12:07.275128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T22:12:07.487475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
50
42.7%
n 12
 
10.3%
none 12
 
10.3%
3 8
 
6.8%
unattended 8
 
6.8%
or 8
 
6.8%
unsupervised 8
 
6.8%
person 8
 
6.8%
5 1
 
0.9%
physically 1
 
0.9%

Most occurring characters

ValueCountFrequency (%)
67
15.1%
e 53
11.9%
- 50
11.3%
n 44
9.9%
o 28
 
6.3%
d 26
 
5.9%
s 26
 
5.9%
N 24
 
5.4%
r 24
 
5.4%
p 16
 
3.6%
Other values (14) 86
19.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 285
64.2%
Space Separator 67
 
15.1%
Dash Punctuation 50
 
11.3%
Uppercase Letter 33
 
7.4%
Decimal Number 9
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 53
18.6%
n 44
15.4%
o 28
9.8%
d 26
9.1%
s 26
9.1%
r 24
8.4%
p 16
 
5.6%
u 16
 
5.6%
t 16
 
5.6%
a 10
 
3.5%
Other values (7) 26
9.1%
Uppercase Letter
ValueCountFrequency (%)
N 24
72.7%
U 8
 
24.2%
P 1
 
3.0%
Decimal Number
ValueCountFrequency (%)
3 8
88.9%
5 1
 
11.1%
Space Separator
ValueCountFrequency (%)
67
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 50
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 318
71.6%
Common 126
 
28.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 53
16.7%
n 44
13.8%
o 28
8.8%
d 26
8.2%
s 26
8.2%
N 24
7.5%
r 24
7.5%
p 16
 
5.0%
u 16
 
5.0%
t 16
 
5.0%
Other values (10) 45
14.2%
Common
ValueCountFrequency (%)
67
53.2%
- 50
39.7%
3 8
 
6.3%
5 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 444
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
67
15.1%
e 53
11.9%
- 50
11.3%
n 44
9.9%
o 28
 
6.3%
d 26
 
5.9%
s 26
 
5.9%
N 24
 
5.4%
r 24
 
5.4%
p 16
 
3.6%
Other values (14) 86
19.4%

estimated_property_loss
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)0.3%
Missing62
Missing (%)6.2%
Memory size7.9 KiB
0.0
936 
100.0
 
1
1500.0
 
1

Length

Max length6
Median length3
Mean length3.0053305
Min length3

Characters and Unicode

Total characters2819
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st row100.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 936
93.6%
100.0 1
 
0.1%
1500.0 1
 
0.1%
(Missing) 62
 
6.2%

Length

2023-05-09T22:12:07.657474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T22:12:07.811380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 936
99.8%
100.0 1
 
0.1%
1500.0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1878
66.6%
. 938
33.3%
1 2
 
0.1%
5 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1881
66.7%
Other Punctuation 938
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1878
99.8%
1 2
 
0.1%
5 1
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 938
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2819
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1878
66.6%
. 938
33.3%
1 2
 
0.1%
5 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2819
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1878
66.6%
. 938
33.3%
1 2
 
0.1%
5 1
 
< 0.1%

structure_type
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)7.7%
Missing974
Missing (%)97.4%
Memory size7.9 KiB
1 -Enclosed building
15 
-
11 

Length

Max length20
Median length20
Mean length11.961538
Min length1

Characters and Unicode

Total characters311
Distinct characters15
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1 -Enclosed building
2nd row1 -Enclosed building
3rd row1 -Enclosed building
4th row1 -Enclosed building
5th row1 -Enclosed building

Common Values

ValueCountFrequency (%)
1 -Enclosed building 15
 
1.5%
- 11
 
1.1%
(Missing) 974
97.4%

Length

2023-05-09T22:12:07.956638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T22:12:08.137922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 15
26.8%
enclosed 15
26.8%
building 15
26.8%
11
19.6%

Most occurring characters

ValueCountFrequency (%)
30
 
9.6%
n 30
 
9.6%
l 30
 
9.6%
d 30
 
9.6%
i 30
 
9.6%
- 26
 
8.4%
1 15
 
4.8%
E 15
 
4.8%
c 15
 
4.8%
o 15
 
4.8%
Other values (5) 75
24.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 225
72.3%
Space Separator 30
 
9.6%
Dash Punctuation 26
 
8.4%
Decimal Number 15
 
4.8%
Uppercase Letter 15
 
4.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 30
13.3%
l 30
13.3%
d 30
13.3%
i 30
13.3%
c 15
6.7%
o 15
6.7%
s 15
6.7%
e 15
6.7%
b 15
6.7%
u 15
6.7%
Space Separator
ValueCountFrequency (%)
30
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%
Decimal Number
ValueCountFrequency (%)
1 15
100.0%
Uppercase Letter
ValueCountFrequency (%)
E 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 240
77.2%
Common 71
 
22.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 30
12.5%
l 30
12.5%
d 30
12.5%
i 30
12.5%
E 15
6.2%
c 15
6.2%
o 15
6.2%
s 15
6.2%
e 15
6.2%
b 15
6.2%
Other values (2) 30
12.5%
Common
ValueCountFrequency (%)
30
42.3%
- 26
36.6%
1 15
21.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 311
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
30
 
9.6%
n 30
 
9.6%
l 30
 
9.6%
d 30
 
9.6%
i 30
 
9.6%
- 26
 
8.4%
1 15
 
4.8%
E 15
 
4.8%
c 15
 
4.8%
o 15
 
4.8%
Other values (5) 75
24.1%

structure_status
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)11.5%
Missing974
Missing (%)97.4%
Memory size7.9 KiB
2 -In normal use
14 
-
11 
4 -Under major renovation
 
1

Length

Max length25
Median length16
Mean length10
Min length1

Characters and Unicode

Total characters260
Distinct characters20
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.8%

Sample

1st row2 -In normal use
2nd row2 -In normal use
3rd row4 -Under major renovation
4th row2 -In normal use
5th row2 -In normal use

Common Values

ValueCountFrequency (%)
2 -In normal use 14
 
1.4%
- 11
 
1.1%
4 -Under major renovation 1
 
0.1%
(Missing) 974
97.4%

Length

2023-05-09T22:12:08.281142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T22:12:08.453035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2 14
19.7%
in 14
19.7%
normal 14
19.7%
use 14
19.7%
11
15.5%
4 1
 
1.4%
under 1
 
1.4%
major 1
 
1.4%
renovation 1
 
1.4%

Most occurring characters

ValueCountFrequency (%)
45
17.3%
n 31
11.9%
- 26
10.0%
o 17
 
6.5%
r 17
 
6.5%
e 16
 
6.2%
a 16
 
6.2%
m 15
 
5.8%
2 14
 
5.4%
s 14
 
5.4%
Other values (10) 49
18.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 159
61.2%
Space Separator 45
 
17.3%
Dash Punctuation 26
 
10.0%
Decimal Number 15
 
5.8%
Uppercase Letter 15
 
5.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 31
19.5%
o 17
10.7%
r 17
10.7%
e 16
10.1%
a 16
10.1%
m 15
9.4%
s 14
8.8%
u 14
8.8%
l 14
8.8%
d 1
 
0.6%
Other values (4) 4
 
2.5%
Decimal Number
ValueCountFrequency (%)
2 14
93.3%
4 1
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
I 14
93.3%
U 1
 
6.7%
Space Separator
ValueCountFrequency (%)
45
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 174
66.9%
Common 86
33.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 31
17.8%
o 17
9.8%
r 17
9.8%
e 16
9.2%
a 16
9.2%
m 15
8.6%
s 14
8.0%
u 14
8.0%
l 14
8.0%
I 14
8.0%
Other values (6) 6
 
3.4%
Common
ValueCountFrequency (%)
45
52.3%
- 26
30.2%
2 14
 
16.3%
4 1
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
45
17.3%
n 31
11.9%
- 26
10.0%
o 17
 
6.5%
r 17
 
6.5%
e 16
 
6.2%
a 16
 
6.2%
m 15
 
5.8%
2 14
 
5.4%
s 14
 
5.4%
Other values (10) 49
18.8%

floor_of_fire_origin
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)40.0%
Missing985
Missing (%)98.5%
Infinite0
Infinite (%)0.0%
Mean3.1333333
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-05-09T22:12:08.621004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.5
median2
Q32.5
95-th percentile8.7
Maximum15
Range14
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.5830288
Coefficient of variation (CV)1.1435198
Kurtosis9.5680769
Mean3.1333333
Median Absolute Deviation (MAD)1
Skewness2.9600409
Sum47
Variance12.838095
MonotonicityNot monotonic
2023-05-09T22:12:08.763921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 7
 
0.7%
1 4
 
0.4%
3 1
 
0.1%
15 1
 
0.1%
5 1
 
0.1%
6 1
 
0.1%
(Missing) 985
98.5%
ValueCountFrequency (%)
1 4
0.4%
2 7
0.7%
3 1
 
0.1%
5 1
 
0.1%
6 1
 
0.1%
15 1
 
0.1%
ValueCountFrequency (%)
15 1
 
0.1%
6 1
 
0.1%
5 1
 
0.1%
3 1
 
0.1%
2 7
0.7%
1 4
0.4%

fire_spread
Categorical

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)30.8%
Missing974
Missing (%)97.4%
Memory size7.9 KiB
-
16 
14 -Floor covering or rug/carpet/mat
00 -Item First Ignited, Other
UU -Undetermined
59 -Rolled, wound material (paper, fabric)
 
1
Other values (3)

Length

Max length42
Median length1
Mean length12.038462
Min length1

Characters and Unicode

Total characters313
Distinct characters44
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)15.4%

Sample

1st row-
2nd row14 -Floor covering or rug/carpet/mat
3rd row59 -Rolled, wound material (paper, fabric)
4th row-
5th row14 -Floor covering or rug/carpet/mat

Common Values

ValueCountFrequency (%)
- 16
 
1.6%
14 -Floor covering or rug/carpet/mat 2
 
0.2%
00 -Item First Ignited, Other 2
 
0.2%
UU -Undetermined 2
 
0.2%
59 -Rolled, wound material (paper, fabric) 1
 
0.1%
76 -Cooking materials, inc. Edible materia 1
 
0.1%
44 -Chips, including wood chips 1
 
0.1%
31 -Mattress, pillow 1
 
0.1%
(Missing) 974
97.4%

Length

2023-05-09T22:12:08.916180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T22:12:09.113990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
16
26.7%
first 2
 
3.3%
chips 2
 
3.3%
14 2
 
3.3%
undetermined 2
 
3.3%
other 2
 
3.3%
ignited 2
 
3.3%
uu 2
 
3.3%
item 2
 
3.3%
00 2
 
3.3%
Other values (22) 26
43.3%

Most occurring characters

ValueCountFrequency (%)
34
 
10.9%
- 26
 
8.3%
e 23
 
7.3%
r 22
 
7.0%
i 20
 
6.4%
t 19
 
6.1%
o 15
 
4.8%
a 13
 
4.2%
n 13
 
4.2%
d 11
 
3.5%
Other values (34) 117
37.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 202
64.5%
Space Separator 34
 
10.9%
Dash Punctuation 26
 
8.3%
Uppercase Letter 21
 
6.7%
Decimal Number 16
 
5.1%
Other Punctuation 12
 
3.8%
Close Punctuation 1
 
0.3%
Open Punctuation 1
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 23
11.4%
r 22
10.9%
i 20
9.9%
t 19
9.4%
o 15
 
7.4%
a 13
 
6.4%
n 13
 
6.4%
d 11
 
5.4%
l 10
 
5.0%
m 9
 
4.5%
Other values (11) 47
23.3%
Uppercase Letter
ValueCountFrequency (%)
U 6
28.6%
F 4
19.0%
I 4
19.0%
O 2
 
9.5%
C 2
 
9.5%
E 1
 
4.8%
R 1
 
4.8%
M 1
 
4.8%
Decimal Number
ValueCountFrequency (%)
4 4
25.0%
0 4
25.0%
1 3
18.8%
6 1
 
6.2%
3 1
 
6.2%
7 1
 
6.2%
9 1
 
6.2%
5 1
 
6.2%
Other Punctuation
ValueCountFrequency (%)
, 7
58.3%
/ 4
33.3%
. 1
 
8.3%
Space Separator
ValueCountFrequency (%)
34
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 223
71.2%
Common 90
28.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 23
 
10.3%
r 22
 
9.9%
i 20
 
9.0%
t 19
 
8.5%
o 15
 
6.7%
a 13
 
5.8%
n 13
 
5.8%
d 11
 
4.9%
l 10
 
4.5%
m 9
 
4.0%
Other values (19) 68
30.5%
Common
ValueCountFrequency (%)
34
37.8%
- 26
28.9%
, 7
 
7.8%
/ 4
 
4.4%
4 4
 
4.4%
0 4
 
4.4%
1 3
 
3.3%
6 1
 
1.1%
3 1
 
1.1%
. 1
 
1.1%
Other values (5) 5
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 313
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
34
 
10.9%
- 26
 
8.3%
e 23
 
7.3%
r 22
 
7.0%
i 20
 
6.4%
t 19
 
6.1%
o 15
 
4.8%
a 13
 
4.2%
n 13
 
4.2%
d 11
 
3.5%
Other values (34) 117
37.4%

no_flame_spead
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)20.0%
Missing985
Missing (%)98.5%
Memory size7.9 KiB
2.0
1.0
3.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)6.7%

Sample

1st row1.0
2nd row3.0
3rd row2.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 9
 
0.9%
1.0 5
 
0.5%
3.0 1
 
0.1%
(Missing) 985
98.5%

Length

2023-05-09T22:12:09.310982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T22:12:09.546965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0 9
60.0%
1.0 5
33.3%
3.0 1
 
6.7%

Most occurring characters

ValueCountFrequency (%)
. 15
33.3%
0 15
33.3%
2 9
20.0%
1 5
 
11.1%
3 1
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30
66.7%
Other Punctuation 15
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15
50.0%
2 9
30.0%
1 5
 
16.7%
3 1
 
3.3%
Other Punctuation
ValueCountFrequency (%)
. 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 15
33.3%
0 15
33.3%
2 9
20.0%
1 5
 
11.1%
3 1
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 15
33.3%
0 15
33.3%
2 9
20.0%
1 5
 
11.1%
3 1
 
2.2%

number_of_floors_with_minimum_damage
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)13.3%
Missing985
Missing (%)98.5%
Memory size7.9 KiB
1.0
13 
0.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 13
 
1.3%
0.0 2
 
0.2%
(Missing) 985
98.5%

Length

2023-05-09T22:12:09.692082image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T22:12:09.846146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 13
86.7%
0.0 2
 
13.3%

Most occurring characters

ValueCountFrequency (%)
0 17
37.8%
. 15
33.3%
1 13
28.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30
66.7%
Other Punctuation 15
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 17
56.7%
1 13
43.3%
Other Punctuation
ValueCountFrequency (%)
. 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 17
37.8%
. 15
33.3%
1 13
28.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 17
37.8%
. 15
33.3%
1 13
28.9%

number_of_floors_with_significant_damage
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)13.3%
Missing985
Missing (%)98.5%
Memory size7.9 KiB
0.0
13 
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 13
 
1.3%
1.0 2
 
0.2%
(Missing) 985
98.5%

Length

2023-05-09T22:12:09.981977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T22:12:10.136578image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 13
86.7%
1.0 2
 
13.3%

Most occurring characters

ValueCountFrequency (%)
0 28
62.2%
. 15
33.3%
1 2
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30
66.7%
Other Punctuation 15
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28
93.3%
1 2
 
6.7%
Other Punctuation
ValueCountFrequency (%)
. 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28
62.2%
. 15
33.3%
1 2
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28
62.2%
. 15
33.3%
1 2
 
4.4%

number_of_floors_with_heavy_damage
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)6.7%
Missing985
Missing (%)98.5%
Memory size7.9 KiB
0.0
15 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 15
 
1.5%
(Missing) 985
98.5%

Length

2023-05-09T22:12:10.269780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T22:12:10.425869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 15
100.0%

Most occurring characters

ValueCountFrequency (%)
0 30
66.7%
. 15
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30
66.7%
Other Punctuation 15
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30
100.0%
Other Punctuation
ValueCountFrequency (%)
. 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30
66.7%
. 15
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30
66.7%
. 15
33.3%

number_of_floors_with_extreme_damage
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)6.7%
Missing985
Missing (%)98.5%
Memory size7.9 KiB
0.0
15 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 15
 
1.5%
(Missing) 985
98.5%

Length

2023-05-09T22:12:10.559504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T22:12:10.715386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 15
100.0%

Most occurring characters

ValueCountFrequency (%)
0 30
66.7%
. 15
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30
66.7%
Other Punctuation 15
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30
100.0%
Other Punctuation
ValueCountFrequency (%)
. 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30
66.7%
. 15
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30
66.7%
. 15
33.3%

detectors_present
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)15.4%
Missing974
Missing (%)97.4%
Memory size7.9 KiB
-
11 
1 -Present
U -Undetermined
N -Not present
 
1

Length

Max length15
Median length14
Mean length7.5
Min length1

Characters and Unicode

Total characters195
Distinct characters16
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.8%

Sample

1st rowU -Undetermined
2nd row1 -Present
3rd row1 -Present
4th row1 -Present
5th rowU -Undetermined

Common Values

ValueCountFrequency (%)
- 11
 
1.1%
1 -Present 8
 
0.8%
U -Undetermined 6
 
0.6%
N -Not present 1
 
0.1%
(Missing) 974
97.4%

Length

2023-05-09T22:12:10.877150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T22:12:11.151332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
11
26.2%
present 9
21.4%
1 8
19.0%
u 6
14.3%
undetermined 6
14.3%
n 1
 
2.4%
not 1
 
2.4%

Most occurring characters

ValueCountFrequency (%)
e 36
18.5%
- 26
13.3%
n 21
10.8%
16
8.2%
t 16
8.2%
r 15
7.7%
U 12
 
6.2%
d 12
 
6.2%
s 9
 
4.6%
1 8
 
4.1%
Other values (6) 24
12.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 123
63.1%
Dash Punctuation 26
 
13.3%
Uppercase Letter 22
 
11.3%
Space Separator 16
 
8.2%
Decimal Number 8
 
4.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 36
29.3%
n 21
17.1%
t 16
13.0%
r 15
12.2%
d 12
 
9.8%
s 9
 
7.3%
m 6
 
4.9%
i 6
 
4.9%
o 1
 
0.8%
p 1
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
U 12
54.5%
P 8
36.4%
N 2
 
9.1%
Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%
Space Separator
ValueCountFrequency (%)
16
100.0%
Decimal Number
ValueCountFrequency (%)
1 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 145
74.4%
Common 50
 
25.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 36
24.8%
n 21
14.5%
t 16
11.0%
r 15
10.3%
U 12
 
8.3%
d 12
 
8.3%
s 9
 
6.2%
P 8
 
5.5%
m 6
 
4.1%
i 6
 
4.1%
Other values (3) 4
 
2.8%
Common
ValueCountFrequency (%)
- 26
52.0%
16
32.0%
1 8
 
16.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 195
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 36
18.5%
- 26
13.3%
n 21
10.8%
16
8.2%
t 16
8.2%
r 15
7.7%
U 12
 
6.2%
d 12
 
6.2%
s 9
 
4.6%
1 8
 
4.1%
Other values (6) 24
12.3%

detector_type
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)19.2%
Missing974
Missing (%)97.4%
Memory size7.9 KiB
-
18 
U -Undetermined
1 -Smoke
0 -Detector type, other
 
1
3 -Combination smoke & heat in single unit
 
1

Length

Max length42
Median length1
Mean length5.8461538
Min length1

Characters and Unicode

Total characters152
Distinct characters30
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)7.7%

Sample

1st row-
2nd row0 -Detector type, other
3rd rowU -Undetermined
4th row1 -Smoke
5th row-

Common Values

ValueCountFrequency (%)
- 18
 
1.8%
U -Undetermined 3
 
0.3%
1 -Smoke 3
 
0.3%
0 -Detector type, other 1
 
0.1%
3 -Combination smoke & heat in single unit 1
 
0.1%
(Missing) 974
97.4%

Length

2023-05-09T22:12:11.331776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T22:12:11.731650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
19
45.2%
smoke 4
 
9.5%
u 3
 
7.1%
undetermined 3
 
7.1%
1 3
 
7.1%
0 1
 
2.4%
detector 1
 
2.4%
type 1
 
2.4%
other 1
 
2.4%
3 1
 
2.4%
Other values (5) 5
 
11.9%

Most occurring characters

ValueCountFrequency (%)
- 26
17.1%
e 19
12.5%
16
10.5%
n 11
 
7.2%
t 10
 
6.6%
o 8
 
5.3%
m 8
 
5.3%
i 8
 
5.3%
d 6
 
3.9%
U 6
 
3.9%
Other values (20) 34
22.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 92
60.5%
Dash Punctuation 26
 
17.1%
Space Separator 16
 
10.5%
Uppercase Letter 11
 
7.2%
Decimal Number 5
 
3.3%
Other Punctuation 2
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 19
20.7%
n 11
12.0%
t 10
10.9%
o 8
8.7%
m 8
8.7%
i 8
8.7%
d 6
 
6.5%
r 5
 
5.4%
k 4
 
4.3%
s 2
 
2.2%
Other values (9) 11
12.0%
Uppercase Letter
ValueCountFrequency (%)
U 6
54.5%
S 3
27.3%
C 1
 
9.1%
D 1
 
9.1%
Decimal Number
ValueCountFrequency (%)
1 3
60.0%
3 1
 
20.0%
0 1
 
20.0%
Other Punctuation
ValueCountFrequency (%)
& 1
50.0%
, 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%
Space Separator
ValueCountFrequency (%)
16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 103
67.8%
Common 49
32.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 19
18.4%
n 11
10.7%
t 10
9.7%
o 8
7.8%
m 8
7.8%
i 8
7.8%
d 6
 
5.8%
U 6
 
5.8%
r 5
 
4.9%
k 4
 
3.9%
Other values (13) 18
17.5%
Common
ValueCountFrequency (%)
- 26
53.1%
16
32.7%
1 3
 
6.1%
& 1
 
2.0%
3 1
 
2.0%
, 1
 
2.0%
0 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 152
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 26
17.1%
e 19
12.5%
16
10.5%
n 11
 
7.2%
t 10
 
6.6%
o 8
 
5.3%
m 8
 
5.3%
i 8
 
5.3%
d 6
 
3.9%
U 6
 
3.9%
Other values (20) 34
22.4%

detector_operation
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)11.5%
Missing974
Missing (%)97.4%
Memory size7.9 KiB
-
18 
2 -Detector operated
1 -Fire too small to activate detector
 
1

Length

Max length38
Median length1
Mean length7.5384615
Min length1

Characters and Unicode

Total characters196
Distinct characters19
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.8%

Sample

1st row-
2nd row2 -Detector operated
3rd row2 -Detector operated
4th row2 -Detector operated
5th row-

Common Values

ValueCountFrequency (%)
- 18
 
1.8%
2 -Detector operated 7
 
0.7%
1 -Fire too small to activate detector 1
 
0.1%
(Missing) 974
97.4%

Length

2023-05-09T22:12:11.899042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T22:12:12.065272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
18
39.1%
detector 8
17.4%
2 7
 
15.2%
operated 7
 
15.2%
1 1
 
2.2%
fire 1
 
2.2%
too 1
 
2.2%
small 1
 
2.2%
to 1
 
2.2%
activate 1
 
2.2%

Most occurring characters

ValueCountFrequency (%)
e 32
16.3%
t 27
13.8%
- 26
13.3%
20
10.2%
o 18
9.2%
r 16
8.2%
a 10
 
5.1%
c 9
 
4.6%
d 8
 
4.1%
p 7
 
3.6%
Other values (9) 23
11.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 134
68.4%
Dash Punctuation 26
 
13.3%
Space Separator 20
 
10.2%
Decimal Number 8
 
4.1%
Uppercase Letter 8
 
4.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 32
23.9%
t 27
20.1%
o 18
13.4%
r 16
11.9%
a 10
 
7.5%
c 9
 
6.7%
d 8
 
6.0%
p 7
 
5.2%
i 2
 
1.5%
l 2
 
1.5%
Other values (3) 3
 
2.2%
Decimal Number
ValueCountFrequency (%)
2 7
87.5%
1 1
 
12.5%
Uppercase Letter
ValueCountFrequency (%)
D 7
87.5%
F 1
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%
Space Separator
ValueCountFrequency (%)
20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 142
72.4%
Common 54
 
27.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 32
22.5%
t 27
19.0%
o 18
12.7%
r 16
11.3%
a 10
 
7.0%
c 9
 
6.3%
d 8
 
5.6%
p 7
 
4.9%
D 7
 
4.9%
i 2
 
1.4%
Other values (5) 6
 
4.2%
Common
ValueCountFrequency (%)
- 26
48.1%
20
37.0%
2 7
 
13.0%
1 1
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 196
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 32
16.3%
t 27
13.8%
- 26
13.3%
20
10.2%
o 18
9.2%
r 16
8.2%
a 10
 
5.1%
c 9
 
4.6%
d 8
 
4.1%
p 7
 
3.6%
Other values (9) 23
11.7%

detector_effectiveness
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)15.4%
Missing974
Missing (%)97.4%
Memory size7.9 KiB
-
19 
1 -Alerted occupants, occupants responded
3 -There were no occupants
2 -Alerted occupants-occ. failed to resond
 
1

Length

Max length42
Median length1
Mean length10.653846
Min length1

Characters and Unicode

Total characters277
Distinct characters25
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.8%

Sample

1st row-
2nd row3 -There were no occupants
3rd row3 -There were no occupants
4th row1 -Alerted occupants, occupants responded
5th row-

Common Values

ValueCountFrequency (%)
- 19
 
1.9%
1 -Alerted occupants, occupants responded 4
 
0.4%
3 -There were no occupants 2
 
0.2%
2 -Alerted occupants-occ. failed to resond 1
 
0.1%
(Missing) 974
97.4%

Length

2023-05-09T22:12:12.217044image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T22:12:12.389481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
19
34.5%
occupants 10
18.2%
alerted 5
 
9.1%
1 4
 
7.3%
responded 4
 
7.3%
3 2
 
3.6%
there 2
 
3.6%
were 2
 
3.6%
no 2
 
3.6%
2 1
 
1.8%
Other values (4) 4
 
7.3%

Most occurring characters

ValueCountFrequency (%)
29
10.5%
e 28
10.1%
- 27
 
9.7%
c 24
 
8.7%
o 20
 
7.2%
n 18
 
6.5%
t 17
 
6.1%
s 16
 
5.8%
p 15
 
5.4%
d 15
 
5.4%
Other values (15) 68
24.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 202
72.9%
Space Separator 29
 
10.5%
Dash Punctuation 27
 
9.7%
Uppercase Letter 7
 
2.5%
Decimal Number 7
 
2.5%
Other Punctuation 5
 
1.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 28
13.9%
c 24
11.9%
o 20
9.9%
n 18
8.9%
t 17
8.4%
s 16
7.9%
p 15
7.4%
d 15
7.4%
r 14
6.9%
a 12
5.9%
Other values (6) 23
11.4%
Decimal Number
ValueCountFrequency (%)
1 4
57.1%
3 2
28.6%
2 1
 
14.3%
Uppercase Letter
ValueCountFrequency (%)
A 5
71.4%
T 2
 
28.6%
Other Punctuation
ValueCountFrequency (%)
, 4
80.0%
. 1
 
20.0%
Space Separator
ValueCountFrequency (%)
29
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 209
75.5%
Common 68
 
24.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 28
13.4%
c 24
11.5%
o 20
9.6%
n 18
8.6%
t 17
8.1%
s 16
7.7%
p 15
7.2%
d 15
7.2%
r 14
6.7%
a 12
 
5.7%
Other values (8) 30
14.4%
Common
ValueCountFrequency (%)
29
42.6%
- 27
39.7%
1 4
 
5.9%
, 4
 
5.9%
3 2
 
2.9%
2 1
 
1.5%
. 1
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 277
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
29
10.5%
e 28
10.1%
- 27
 
9.7%
c 24
 
8.7%
o 20
 
7.2%
n 18
 
6.5%
t 17
 
6.1%
s 16
 
5.8%
p 15
 
5.4%
d 15
 
5.4%
Other values (15) 68
24.5%

detector_failure_reason
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.8%
Missing974
Missing (%)97.4%
Memory size7.9 KiB
-
26 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters26
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 26
 
2.6%
(Missing) 974
97.4%

Length

2023-05-09T22:12:12.549380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T22:12:12.686524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
26
100.0%

Most occurring characters

ValueCountFrequency (%)
- 26
100.0%

Most occurring categories

ValueCountFrequency (%)
Dash Punctuation 26
100.0%

Most frequent character per category

Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 26
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 26
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 26
100.0%

automatic_extinguishing_system_present
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)11.5%
Missing974
Missing (%)97.4%
Memory size7.9 KiB
N -None Present
11 
-
11 
1 -Present

Length

Max length15
Median length10
Mean length8.3076923
Min length1

Characters and Unicode

Total characters216
Distinct characters11
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN -None Present
2nd rowN -None Present
3rd rowN -None Present
4th row1 -Present
5th rowN -None Present

Common Values

ValueCountFrequency (%)
N -None Present 11
 
1.1%
- 11
 
1.1%
1 -Present 4
 
0.4%
(Missing) 974
97.4%

Length

2023-05-09T22:12:12.811186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T22:12:12.971164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
present 15
28.8%
n 11
21.2%
none 11
21.2%
11
21.2%
1 4
 
7.7%

Most occurring characters

ValueCountFrequency (%)
e 41
19.0%
26
12.0%
- 26
12.0%
n 26
12.0%
N 22
10.2%
P 15
 
6.9%
r 15
 
6.9%
s 15
 
6.9%
t 15
 
6.9%
o 11
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 123
56.9%
Uppercase Letter 37
 
17.1%
Space Separator 26
 
12.0%
Dash Punctuation 26
 
12.0%
Decimal Number 4
 
1.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 41
33.3%
n 26
21.1%
r 15
 
12.2%
s 15
 
12.2%
t 15
 
12.2%
o 11
 
8.9%
Uppercase Letter
ValueCountFrequency (%)
N 22
59.5%
P 15
40.5%
Space Separator
ValueCountFrequency (%)
26
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%
Decimal Number
ValueCountFrequency (%)
1 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 160
74.1%
Common 56
 
25.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 41
25.6%
n 26
16.2%
N 22
13.8%
P 15
 
9.4%
r 15
 
9.4%
s 15
 
9.4%
t 15
 
9.4%
o 11
 
6.9%
Common
ValueCountFrequency (%)
26
46.4%
- 26
46.4%
1 4
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 41
19.0%
26
12.0%
- 26
12.0%
n 26
12.0%
N 22
10.2%
P 15
 
6.9%
r 15
 
6.9%
s 15
 
6.9%
t 15
 
6.9%
o 11
 
5.1%

automatic_extinguishing_sytem_type
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.8%
Missing974
Missing (%)97.4%
Memory size7.9 KiB
-
26 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters26
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 26
 
2.6%
(Missing) 974
97.4%

Length

2023-05-09T22:12:13.106559image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T22:12:13.253893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
26
100.0%

Most occurring characters

ValueCountFrequency (%)
- 26
100.0%

Most occurring categories

ValueCountFrequency (%)
Dash Punctuation 26
100.0%

Most frequent character per category

Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 26
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 26
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 26
100.0%

automatic_extinguishing_sytem_perfomance
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.8%
Missing974
Missing (%)97.4%
Memory size7.9 KiB
-
26 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters26
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 26
 
2.6%
(Missing) 974
97.4%

Length

2023-05-09T22:12:13.380107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T22:12:13.530912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
26
100.0%

Most occurring characters

ValueCountFrequency (%)
- 26
100.0%

Most occurring categories

ValueCountFrequency (%)
Dash Punctuation 26
100.0%

Most frequent character per category

Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 26
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 26
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 26
100.0%

automatic_extinguishing_sytem_failure_reason
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.8%
Missing974
Missing (%)97.4%
Memory size7.9 KiB
-
26 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters26
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 26
 
2.6%
(Missing) 974
97.4%

Length

2023-05-09T22:12:13.651801image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T22:12:13.795682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
26
100.0%

Most occurring characters

ValueCountFrequency (%)
- 26
100.0%

Most occurring categories

ValueCountFrequency (%)
Dash Punctuation 26
100.0%

Most frequent character per category

Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 26
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 26
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 26
100.0%

number_of_sprinkler_heads_operating
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)6.7%
Missing985
Missing (%)98.5%
Memory size7.9 KiB
0.0
15 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 15
 
1.5%
(Missing) 985
98.5%

Length

2023-05-09T22:12:13.912886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T22:12:14.085888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 15
100.0%

Most occurring characters

ValueCountFrequency (%)
0 30
66.7%
. 15
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30
66.7%
Other Punctuation 15
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30
100.0%
Other Punctuation
ValueCountFrequency (%)
. 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30
66.7%
. 15
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30
66.7%
. 15
33.3%

box
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)90.9%
Missing989
Missing (%)98.9%
Infinite0
Infinite (%)0.0%
Mean2457.1818
Minimum1113
Maximum7543
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-05-09T22:12:14.207217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1113
5-th percentile1277.5
Q11826.5
median2146
Q32251.5
95-th percentile4942.5
Maximum7543
Range6430
Interquartile range (IQR)425

Descriptive statistics

Standard deviation1733.9577
Coefficient of variation (CV)0.70566928
Kurtosis9.5048679
Mean2457.1818
Median Absolute Deviation (MAD)180
Skewness2.9807573
Sum27029
Variance3006609.4
MonotonicityNot monotonic
2023-05-09T22:12:14.347185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2112 2
 
0.2%
1442 1
 
0.1%
1113 1
 
0.1%
2175 1
 
0.1%
7543 1
 
0.1%
2342 1
 
0.1%
2177 1
 
0.1%
2326 1
 
0.1%
1541 1
 
0.1%
2146 1
 
0.1%
(Missing) 989
98.9%
ValueCountFrequency (%)
1113 1
0.1%
1442 1
0.1%
1541 1
0.1%
2112 2
0.2%
2146 1
0.1%
2175 1
0.1%
2177 1
0.1%
2326 1
0.1%
2342 1
0.1%
7543 1
0.1%
ValueCountFrequency (%)
7543 1
0.1%
2342 1
0.1%
2326 1
0.1%
2177 1
0.1%
2175 1
0.1%
2146 1
0.1%
2112 2
0.2%
1541 1
0.1%
1442 1
0.1%
1113 1
0.1%

Interactions

2023-05-09T22:11:46.954986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:27.647969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:29.512379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:31.340612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:33.179856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:35.298526image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:37.174756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:39.153307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:41.300559image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:45.184818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:47.143052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:27.837956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:29.708681image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:31.527429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:33.373067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:35.487577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:37.375290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:39.394334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:41.517550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:45.400015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:47.316332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:28.035042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:29.898399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:31.717775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:33.561040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:35.685021image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:37.558882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:39.654889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:42.071932image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:45.564527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:47.507991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:28.225855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:30.083572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:31.907157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:33.761621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:35.887986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:37.748894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:39.903238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:42.378506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:45.710575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:47.690548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:28.418704image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:30.278575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:32.098319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:33.957920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:36.106611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:37.942585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:40.120062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:42.869212image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:45.852241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:47.898657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:28.629800image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:30.466971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:32.296994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:34.169673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:36.307306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:38.139406image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:40.334804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:43.425541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:46.002752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:48.099172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:28.823231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:30.657074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:32.483583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:34.600329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:36.494028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:38.343997image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:40.526110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:43.945389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:46.189434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:48.298029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:29.013940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:30.847299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:32.682719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:34.799885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:36.687718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:38.608399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:40.737765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:44.543727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:46.382077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:48.477495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:29.185519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:31.018282image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:32.854511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:34.972636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:36.856556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:38.813164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:40.922484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:44.786131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:46.594374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:48.636940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:29.347053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:31.176131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:33.011524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:35.119463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:37.008047image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:38.978910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:41.111247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:44.958372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-09T22:11:46.779791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-05-09T22:12:14.598468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
incident_numberidcall_numberzipcodesuppression_unitssuppression_personnelems_personnelsupervisor_districtfloor_of_fire_originboxincident_datecitybattalionstation_areaems_unitsother_unitsother_personnelfirst_unit_on_sceneprimary_situationaction_taken_primaryaction_taken_secondaryaction_taken_otherdetector_alerted_occupantsproperty_useneighborhood_districtestimated_contents_lossarea_of_fire_originignition_causeignition_factor_primaryheat_sourceitem_first_ignitedhuman_factors_associated_with_ignitionestimated_property_lossstructure_typestructure_statusfire_spreadno_flame_speadnumber_of_floors_with_minimum_damagenumber_of_floors_with_significant_damagedetectors_presentdetector_typedetector_operationdetector_effectivenessautomatic_extinguishing_system_present
incident_number1.0001.0001.000-0.0270.0600.060-0.0120.0950.0270.0730.9840.1390.0680.2010.0370.0000.0000.2170.2730.2000.0000.0000.0000.2440.1670.9990.0000.0000.4840.3400.2240.2830.9990.0000.0000.0000.0000.0000.0000.1180.0000.0000.0000.000
id1.0001.0001.000-0.0270.0600.060-0.0120.0950.0270.0730.9840.1390.0680.2010.0370.0000.0000.2170.2730.2000.0000.0000.0000.2440.1670.9990.0000.0000.4840.3400.2240.2830.9990.0000.0000.0000.0000.0000.0000.1180.0000.0000.0000.000
call_number1.0001.0001.000-0.0270.0600.060-0.0120.0950.0270.1050.9840.1390.0680.2010.0370.0000.0000.2170.2730.2000.0000.0000.0000.2440.1670.9990.0000.0000.4840.3400.2240.2830.9990.0000.0000.0000.0000.0000.0000.1180.0000.0000.0000.000
zipcode-0.027-0.027-0.0271.000-0.122-0.114-0.007-0.264-0.561-0.1130.0860.4840.4400.7180.0340.0350.0950.6910.1700.1080.0000.0000.0000.2070.6200.0000.2760.3680.1830.3700.4500.0000.0000.4430.3730.2820.0000.2660.2660.3090.1850.0000.0000.363
suppression_units0.0600.0600.060-0.1221.0000.940-0.023-0.1320.0170.3940.0000.0630.0000.0310.1170.0940.0940.0710.3530.0800.2440.2390.0720.0000.0980.0000.6330.2360.4740.3830.4890.3200.0000.5670.4380.5910.3220.0000.0000.4490.0000.0000.2930.380
suppression_personnel0.0600.0600.060-0.1140.9401.000-0.070-0.1370.0120.3940.0770.0810.0870.0690.2120.2810.2970.0910.5070.1350.2250.2970.2780.1400.0930.0000.3860.0880.3450.4250.3650.4350.0000.4200.3270.4720.2570.4490.4490.3140.1110.0000.0550.162
ems_personnel-0.012-0.012-0.012-0.007-0.023-0.0701.0000.0070.072-0.3740.0000.1150.0000.0000.9550.1430.1060.3130.3020.1920.3610.0000.0460.1510.0000.0940.0000.0000.0000.0000.0000.2600.1260.0000.0000.2400.0000.0000.0000.3790.0000.0000.3730.000
supervisor_district0.0950.0950.095-0.264-0.132-0.1370.0071.0000.354NaN0.2360.8470.7340.8460.0000.1370.1000.7740.2420.1740.1520.0000.0000.1770.9041.0000.6670.1180.0000.1540.2360.1291.0000.0000.0000.2501.0001.0001.0000.0000.0000.0000.0000.000
floor_of_fire_origin0.0270.0270.027-0.5610.0170.0120.0720.3541.000NaN0.4571.0000.0000.0000.4520.0000.0000.0000.0000.0000.4520.5120.0000.0000.0000.9130.0000.3040.3190.5050.5310.4520.9131.0000.0000.1130.0000.0000.0000.0000.0000.0000.4310.410
box0.0730.0730.105-0.1130.3940.394-0.374NaNNaN1.0000.0001.0000.9350.5000.0000.0000.0001.0000.0000.0001.0001.0001.0000.0000.7151.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0000.0000.0001.0001.0001.0001.0001.000
incident_date0.9840.9840.9840.0860.0000.0770.0000.2360.4570.0001.0000.1950.0660.0880.0660.0000.0180.1630.0850.0990.0000.0000.0000.0880.0810.5620.4200.0660.2030.3670.3500.3370.6990.6280.2220.4920.0000.0000.0000.6480.5330.6960.5620.619
city0.1390.1390.1390.4840.0630.0810.1150.8471.0001.0000.1951.0000.5850.7270.1230.0000.0000.7170.1260.0650.0000.0000.0000.4250.5670.0001.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
battalion0.0680.0680.0680.4400.0000.0870.0000.7340.0000.9350.0660.5851.0000.7950.0000.0350.1020.7510.1410.0860.0650.0000.0000.1780.7640.0000.0650.3600.2200.3610.2720.0000.0000.2610.1290.4240.0000.3530.3530.5900.2140.2010.0000.378
station_area0.2010.2010.2010.7180.0310.0690.0000.8460.0000.5000.0880.7270.7951.0000.0000.0000.1330.8330.1260.0920.0260.0000.0780.1680.6380.1100.2920.1790.3050.3370.3820.0000.0000.0000.0000.3220.0000.3920.3920.2330.0000.2630.0000.000
ems_units0.0370.0370.0370.0340.1170.2120.9550.0000.4520.0000.0660.1230.0000.0001.0000.2450.3250.4310.3560.2460.4050.0000.0600.2360.0000.0810.0000.0000.0000.0000.0000.2600.1070.0000.0000.2400.0000.0000.0000.3790.0000.0000.3730.000
other_units0.0000.0000.0000.0350.0940.2810.1430.1370.0000.0000.0000.0000.0350.0000.2451.0000.8250.0000.5670.0000.0000.0000.0000.4180.0000.0000.0000.0000.2260.4900.0000.0000.0000.0290.0000.5020.0000.0000.0000.6650.0000.0000.0000.246
other_personnel0.0000.0000.0000.0950.0940.2970.1060.1000.0000.0000.0180.0000.1020.1330.3250.8251.0000.0880.2500.0190.0000.0000.0000.2120.1240.0000.0000.0000.2260.4900.0000.0000.0000.0290.0000.5020.0000.0000.0000.6650.0000.0000.0000.246
first_unit_on_scene0.2170.2170.2170.6910.0710.0910.3130.7740.0001.0000.1630.7170.7510.8330.4310.0000.0881.0000.2080.2000.1480.0000.0000.1720.5950.0000.2200.0000.1290.1520.2070.0000.0000.0000.0000.0000.3920.2770.2770.0720.0000.0000.0000.177
primary_situation0.2730.2730.2730.1700.3530.5070.3020.2420.0000.0000.0850.1260.1410.1260.3560.5670.2500.2081.0000.3930.3800.0000.6670.2110.1270.0000.4100.3150.0000.0870.5440.0000.0000.8420.4490.0000.0000.0000.0000.3200.0000.0000.0000.475
action_taken_primary0.2000.2000.2000.1080.0800.1350.1920.1740.0000.0000.0990.0650.0860.0920.2460.0000.0190.2000.3931.0000.4340.1930.3190.0960.0900.0000.2800.1600.0000.0000.1050.0000.0000.4920.2580.0000.3670.1330.1330.2570.3490.6260.0000.350
action_taken_secondary0.0000.0000.0000.0000.2440.2250.3610.1520.4521.0000.0000.0000.0650.0260.4050.0000.0000.1480.3800.4341.0000.6190.0000.1790.0610.0000.0000.0460.2650.4370.5920.3790.0000.0000.6590.6840.0000.0000.0000.0000.0000.0000.3160.149
action_taken_other0.0000.0000.0000.0000.2390.2970.0000.0000.5121.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1930.6191.0000.0000.0000.0000.0000.2410.0000.0710.5170.4640.0700.0000.0000.6890.7070.0000.0000.0000.0000.0310.0000.3700.246
detector_alerted_occupants0.0000.0000.0000.0000.0720.2780.0460.0000.0001.0000.0000.0000.0000.0780.0600.0000.0000.0000.6670.3190.0000.0001.0000.2000.0580.1560.1760.0000.3400.2490.3360.0600.0000.0000.0000.4700.0000.0000.0000.4050.0000.0000.0000.187
property_use0.2440.2440.2440.2070.0000.1400.1510.1770.0000.0000.0880.4250.1780.1680.2360.4180.2120.1720.2110.0960.1790.0000.2001.0000.1070.0000.3730.3120.2500.4780.3220.0000.0000.5580.1100.0000.6220.4760.4760.4890.4820.5330.0000.340
neighborhood_district0.1670.1670.1670.6200.0980.0930.0000.9040.0000.7150.0810.5670.7640.6380.0000.0000.1240.5950.1270.0900.0610.0000.0580.1071.0000.1670.3740.4310.4220.3320.3330.0000.1940.1870.0000.4780.0000.3920.3920.4650.2310.4030.0000.412
estimated_contents_loss0.9990.9990.9990.0000.0000.0000.0941.0000.9131.0000.5620.0000.0000.1100.0810.0000.0000.0000.0000.0000.0000.0000.1560.0000.1671.0000.0000.1590.5260.5040.0000.5981.0000.0000.0000.0000.0000.0000.0000.0000.0310.0000.6480.000
area_of_fire_origin0.0000.0000.0000.2760.6330.3860.0000.6670.0001.0000.4201.0000.0650.2920.0000.0000.0000.2200.4100.2800.0000.2410.1760.3730.3740.0001.0000.5210.3160.4530.6410.2490.0000.8900.7310.4710.0000.4760.4760.5160.4960.7210.2040.628
ignition_cause0.0000.0000.0000.3680.2360.0880.0000.1180.3041.0000.0661.0000.3600.1790.0000.0000.0000.0000.3150.1600.0460.0000.0000.3120.4310.1590.5211.0000.4090.5000.5110.4560.2700.9350.6190.0000.0000.0000.0000.5150.4980.7520.0000.664
ignition_factor_primary0.4840.4840.4840.1830.4740.3450.0000.0000.3191.0000.2031.0000.2200.3050.0000.2260.2260.1290.0000.0000.2650.0710.3400.2500.4220.5260.3160.4091.0000.3420.4130.5130.6250.6030.7580.5860.0000.0000.0000.4940.2890.1570.3970.484
heat_source0.3400.3400.3400.3700.3830.4250.0000.1540.5051.0000.3671.0000.3610.3370.0000.4900.4900.1520.0870.0000.4370.5170.2490.4780.3320.5040.4530.5000.3421.0000.5860.2720.1810.8420.5230.5950.0000.0000.0000.7650.4920.7460.2700.723
item_first_ignited0.2240.2240.2240.4500.4890.3650.0000.2360.5311.0000.3501.0000.2720.3820.0000.0000.0000.2070.5440.1050.5920.4640.3360.3220.3330.0000.6410.5110.4130.5861.0000.2370.0000.8160.8340.7350.0000.6790.6790.4910.5640.7060.3720.670
human_factors_associated_with_ignition0.2830.2830.2830.0000.3200.4350.2600.1290.4521.0000.3371.0000.0000.0000.2600.0000.0000.0000.0000.0000.3790.0700.0600.0000.0000.5980.2490.4560.5130.2720.2371.0000.7010.7280.5130.4200.0000.0000.0000.4220.3690.1920.6280.589
estimated_property_loss0.9990.9990.9990.0000.0000.0000.1261.0000.9131.0000.6990.0000.0000.0000.1070.0000.0000.0000.0000.0000.0000.0000.0000.0000.1941.0000.0000.2700.6250.1810.0000.7011.0000.0000.0000.0000.0000.0000.0000.0000.0310.0000.6480.000
structure_type0.0000.0000.0000.4430.5670.4200.0000.0001.0001.0000.6281.0000.2610.0000.0000.0290.0290.0000.8420.4920.0000.0000.0000.5580.1870.0000.8900.9350.6030.8420.8160.7280.0001.0000.9790.4311.0001.0001.0000.9570.4160.5060.3960.979
structure_status0.0000.0000.0000.3730.4380.3270.0000.0000.0001.0000.2221.0000.1290.0000.0000.0000.0000.0000.4490.2580.6590.6890.0000.1100.0000.0000.7310.6190.7580.5230.8340.5130.0000.9791.0000.6920.0000.0000.0000.6690.3840.3510.5070.686
fire_spread0.0000.0000.0000.2820.5910.4720.2400.2500.1131.0000.4921.0000.4240.3220.2400.5020.5020.0000.0000.0000.6840.7070.4700.0000.4780.0000.4710.0000.5860.5950.7350.4200.0000.4310.6921.0000.5090.7340.7340.5550.2810.0000.5060.495
no_flame_spead0.0000.0000.0000.0000.3220.2570.0001.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.3920.0000.3670.0000.0000.0000.6220.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.5091.0000.6090.6090.0000.5760.0000.2660.000
number_of_floors_with_minimum_damage0.0000.0000.0000.2660.0000.4490.0001.0000.0000.0000.0001.0000.3530.3920.0000.0000.0000.2770.0000.1330.0000.0000.0000.4760.3920.0000.4760.0000.0000.0000.6790.0000.0001.0000.0000.7340.6091.0000.6840.0000.4870.0000.0000.000
number_of_floors_with_significant_damage0.0000.0000.0000.2660.0000.4490.0001.0000.0000.0000.0001.0000.3530.3920.0000.0000.0000.2770.0000.1330.0000.0000.0000.4760.3920.0000.4760.0000.0000.0000.6790.0000.0001.0000.0000.7340.6090.6841.0000.0000.4870.0000.0000.000
detectors_present0.1180.1180.1180.3090.4490.3140.3790.0000.0001.0000.6481.0000.5900.2330.3790.6650.6650.0720.3200.2570.0000.0000.4050.4890.4650.0000.5160.5150.4940.7650.4910.4220.0000.9570.6690.5550.0000.0000.0001.0000.4440.6430.4210.724
detector_type0.0000.0000.0000.1850.0000.1110.0000.0000.0001.0000.5331.0000.2140.0000.0000.0000.0000.0000.0000.3490.0000.0310.0000.4820.2310.0310.4960.4980.2890.4920.5640.3690.0310.4160.3840.2810.5760.4870.4870.4441.0000.9560.6700.398
detector_operation0.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.6961.0000.2010.2630.0000.0000.0000.0000.0000.6260.0000.0000.0000.5330.4030.0000.7210.7520.1570.7460.7060.1920.0000.5060.3510.0000.0000.0000.0000.6430.9561.0000.6430.322
detector_effectiveness0.0000.0000.0000.0000.2930.0550.3730.0000.4311.0000.5621.0000.0000.0000.3730.0000.0000.0000.0000.0000.3160.3700.0000.0000.0000.6480.2040.0000.3970.2700.3720.6280.6480.3960.5070.5060.2660.0000.0000.4210.6700.6431.0000.311
automatic_extinguishing_system_present0.0000.0000.0000.3630.3800.1620.0000.0000.4101.0000.6191.0000.3780.0000.0000.2460.2460.1770.4750.3500.1490.2460.1870.3400.4120.0000.6280.6640.4840.7230.6700.5890.0000.9790.6860.4950.0000.0000.0000.7240.3980.3220.3111.000

Missing values

2023-05-09T22:11:49.113461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-09T22:11:50.213954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-05-09T22:11:52.392111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

incident_numberexposure_numberidaddressincident_datecall_numberalarm_dttmarrival_dttmclose_dttmcityzipcodebattalionstation_areasuppression_unitssuppression_personnelems_unitsems_personnelother_unitsother_personnelfirst_unit_on_scenefire_fatalitiesfire_injuriescivilian_fatalitiescivilian_injuriesnumber_of_alarmsprimary_situationmutual_aidaction_taken_primaryaction_taken_secondaryaction_taken_otherdetector_alerted_occupantsproperty_usesupervisor_districtneighborhood_districtpointestimated_contents_lossarea_of_fire_originignition_causeignition_factor_primaryignition_factor_secondaryheat_sourceitem_first_ignitedhuman_factors_associated_with_ignitionestimated_property_lossstructure_typestructure_statusfloor_of_fire_originfire_spreadno_flame_speadnumber_of_floors_with_minimum_damagenumber_of_floors_with_significant_damagenumber_of_floors_with_heavy_damagenumber_of_floors_with_extreme_damagedetectors_presentdetector_typedetector_operationdetector_effectivenessdetector_failure_reasonautomatic_extinguishing_system_presentautomatic_extinguishing_sytem_typeautomatic_extinguishing_sytem_perfomanceautomatic_extinguishing_sytem_failure_reasonnumber_of_sprinkler_heads_operatingbox
08028304080283040150 Elsie St.2008-04-01T00:00:00809202572008-04-01T18:06:372008-04-01T18:15:192008-04-01T18:21:48SF94110B0611140000E1100001412 - Gas leak (natural gas or LPG)None86 - Investigate---962 - Residential street, road or residential dr9.0Bernal Heights{'type': 'Point', 'coordinates': [-122.41837339, 37.74208979]}NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1802830308028303085 Turner Tr.2008-04-01T00:00:00809202562008-04-01T18:00:522008-04-01T18:06:302008-04-01T18:22:18SF94107B1037140000E3700001552 - Police matterNone76 - Provide water---960 - Street, otherNaNPotrero Hill{'type': 'Point', 'coordinates': [-122.39489, 37.756291]}NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
28028309080283090175 6th St.2008-04-01T00:00:00809202622008-04-01T18:42:062008-04-01T18:45:232008-04-01T18:53:25SF94105B030110350000E0100001210 - Steam Rupture, steam, otherNone86 - Investigate---429 - Multifamily dwellingsNaNSouth of Market{'type': 'Point', 'coordinates': [-122.407468, 37.78008]}NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
38028314080283140633 Hayes St.2008-04-01T00:00:00809202682008-04-01T19:03:522008-04-01T19:08:392008-04-01T19:35:36SF94102B0236140000E3600001522 - Water or steam leakNone64 - Shut down system---400 - Residential, other5.0Hayes Valley{'type': 'Point', 'coordinates': [-122.42684908, 37.77612642]}NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
4802831908028319027th Av. / Cabrillo St.2008-04-01T00:00:00809202732008-04-01T19:16:122008-04-01T19:23:482008-04-01T19:28:49SF94121B0714140000E1400001520 - Water problem, otherNone00 - Action taken, other---960 - Street, otherNaNOuter Richmond{'type': 'Point', 'coordinates': [-122.4863941, 37.77428492]}NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
58028337080283370165 Belgrave Av.2008-04-01T00:00:00809202942008-04-01T20:25:002008-04-01T20:31:412008-04-01T20:51:22SF94117B05123100000E1200001733 - Smoke detector activation/malfunctionNone86 - Investigate---400 - Residential, otherNaNInner Sunset{'type': 'Point', 'coordinates': [-122.4481912, 37.7597267]}NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
68028329080283290Grant Av. / Post St.2008-04-01T00:00:00809202852008-04-01T20:09:552008-04-01T20:12:292008-04-01T20:13:09SF94109B0101290000E0100001711 - Municipal alarm system, Street Box FalseNone86 - Investigate---963 - Street or road in commercial areaNaNFinancial District/South Beach{'type': 'Point', 'coordinates': [-122.405223, 37.788694]}NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
78028350080283500Cortland Av. / Andover St.2008-04-01T00:00:00809203092008-04-01T21:16:252008-04-01T21:18:322008-04-01T21:19:43SF94110B0632290000E3200001711 - Municipal alarm system, Street Box FalseNone86 - Investigate---960 - Street, other9.0Bernal Heights{'type': 'Point', 'coordinates': [-122.416457, 37.739056]}NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
880283550802835502nd St. / Brannan St.2008-04-01T00:00:00809203152008-04-01T21:55:382008-04-01T22:00:332008-04-01T22:07:20SF94107B0308140000T0800001522 - Water or steam leakNone86 - Investigate---963 - Street or road in commercial areaNaNFinancial District/South Beach{'type': 'Point', 'coordinates': [-122.392082, 37.781846]}NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
98028352080283520300 Ortega St.2008-04-01T00:00:00809203112008-04-01T21:17:592008-04-01T21:24:232008-04-01T21:31:37SF94122B08223100000E2200001113 - Cooking fire, confined to containerNone86 - Investigate--1 - Detector alerted occupants400 - Residential, otherNaNInner Sunset{'type': 'Point', 'coordinates': [-122.46678342, 37.75300898]}1.024 - Cooking area, kitchen2 - Unintentional12 - Heat source too close to combustibles-81 - Heat; direct flame or convection76 - Cooking materials, inc. Edible materi3 - Unattended or unsupervised personNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
incident_numberexposure_numberidaddressincident_datecall_numberalarm_dttmarrival_dttmclose_dttmcityzipcodebattalionstation_areasuppression_unitssuppression_personnelems_unitsems_personnelother_unitsother_personnelfirst_unit_on_scenefire_fatalitiesfire_injuriescivilian_fatalitiescivilian_injuriesnumber_of_alarmsprimary_situationmutual_aidaction_taken_primaryaction_taken_secondaryaction_taken_otherdetector_alerted_occupantsproperty_usesupervisor_districtneighborhood_districtpointestimated_contents_lossarea_of_fire_originignition_causeignition_factor_primaryignition_factor_secondaryheat_sourceitem_first_ignitedhuman_factors_associated_with_ignitionestimated_property_lossstructure_typestructure_statusfloor_of_fire_originfire_spreadno_flame_speadnumber_of_floors_with_minimum_damagenumber_of_floors_with_significant_damagenumber_of_floors_with_heavy_damagenumber_of_floors_with_extreme_damagedetectors_presentdetector_typedetector_operationdetector_effectivenessdetector_failure_reasonautomatic_extinguishing_system_presentautomatic_extinguishing_sytem_typeautomatic_extinguishing_sytem_perfomanceautomatic_extinguishing_sytem_failure_reasonnumber_of_sprinkler_heads_operatingbox
9903003584030035840Geary Bl. / Webster St.2003-01-12T00:00:00301202712003-01-12T19:49:232003-01-12T19:52:002003-01-12T19:56:42SF94115B0405140000E0500001118 - Trash or rubbish fire, containedNone86 - Investigate--U - Unknown960 - Street, other5.0Western Addition{'type': 'Point', 'coordinates': [-122.431207, 37.784568]}0.0NaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9913003585030035850400 11th St.2003-01-12T00:00:00301202722003-01-12T19:51:212003-01-12T19:54:282003-01-12T19:58:07SF94105B0236140000E3600001131 - Passenger vehicle fireNone11 - Extinguish---960 - Street, otherNaNMission{'type': 'Point', 'coordinates': [-122.4140296, 37.77178817]}0.083 - Engine area, running gear, wheel areaU - Cause undetermined after investigation--68 - Backfire from internal combustion eng62 - Flam. liq/gas-in/from engine or burne-0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9923003587030035870921 Alvarado St.2003-01-12T00:00:00301202742003-01-12T20:07:242003-01-12T20:09:442003-01-12T20:11:32SF94114B0624140000E2400001735 - Alarm system sounded due to malfunctionNone86 - Investigate---419 - 1 or 2 family dwellingNaNNoe Valley{'type': 'Point', 'coordinates': [-122.44165728, 37.75300986]}0.0NaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9933004084030040840Sacramento St. / Van Ness Av.2003-01-14T00:00:00301402592003-01-14T14:05:092003-01-14T14:07:452003-01-14T15:15:08SF94109B0441141200E4100001323 - Auto/Ped. Accident (Veh.)None30 - Emergency medical services, other---962 - Residential street, road or residential drNaNPacific Heights{'type': 'Point', 'coordinates': [-122.422517, 37.791289]}0.0NaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
99430040890300408903315 20th St.2003-01-14T00:00:00301402672003-01-14T14:20:002003-01-14T14:23:112003-01-14T14:33:31SF94110B06074151200E0700001651 - Smoke scare, odor of smokeNone86 - Investigate---557 - Per. service; inc. barber/beauty shopNaNMission{'type': 'Point', 'coordinates': [-122.41502979, 37.75873914]}0.0NaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9953004091030040910680 Prentiss St.2003-01-14T00:00:00301402712003-01-14T14:31:052003-01-14T14:39:492003-01-14T15:26:52SF94110B1032120000AR100001111 - Building fireNone80 - Information/invest. & enforcement, other---300 - Health care/Detentino & correction, oth.9.0Bernal Heights{'type': 'Point', 'coordinates': [-122.41262492, 37.73481955]}0.002 - Exterior stairway, ramp, or fire esca1 - Intentional--65 - Cigarette lighter10 - Structural component or finish, other-0.01 -Enclosed building2 -In normal use1.0-1.01.00.00.00.01 -Present3 -Combination smoke & heat in single unit1 -Fire too small to activate detector--N -None Present---0.0NaN
9963004092030040920214 Van Ness Av.2003-01-14T00:00:00301402762003-01-14T14:37:382003-01-14T14:40:252003-01-14T14:43:55SF94105B02363110000E3600001740 - Unintentional alarm, otherNone86 - Investigate---429 - Multifamily dwellings6.0Tenderloin{'type': 'Point', 'coordinates': [-122.419656, 37.777531]}0.0NaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9973004093030040930101 Blanken Av.2003-01-14T00:00:00301402772003-01-14T14:38:562003-01-14T14:43:052003-01-14T14:48:05SF94134B10443120000E4400001711 - Municipal alarm system, Street Box FalseNone86 - Investigate---419 - 1 or 2 family dwellingNaNBayview Hunters Point{'type': 'Point', 'coordinates': [-122.400681, 37.712063]}0.0NaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
998300409403004094021st Av. / Judah St.2003-01-14T00:00:00301402782003-01-14T14:40:252003-01-14T14:44:512003-01-14T14:44:51SF94116B0822260000E2200001700 - False alarm or false call, otherNone86 - Investigate---963 - Street or road in commercial areaNaNSunset/Parkside{'type': 'Point', 'coordinates': [-122.479138, 37.761605]}0.0NaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9993004098030040980Arlington St. / Randall St.2003-01-14T00:00:00301402842003-01-14T14:53:572003-01-14T14:56:592003-01-14T14:57:06SF94110B0632140000T1100001711 - Municipal alarm system, Street Box FalseNone86 - Investigate---960 - Street, otherNaNGlen Park{'type': 'Point', 'coordinates': [-122.4245797, 37.73984966]}0.0NaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN